Karla

Senior Communications Specialist

Atono is the product engineering platform that keeps product context connected to the work.

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AI Product Development   It's an Authoring Problem, Not a Review Problem

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Living Product Requirements Flow

To keep a PRD in sync with code, don't make keeping it current a separate task someone has to remember to do. Tie the specification to the work itself, ground it in the real system, and check what shipped against it. The tools below handle this in different ways, and each comes with a trade-off, usually between keeping the spec close to the code and keeping it easy for everyone to use.

Most PRDs are accurate the day they're approved. Then implementation starts, and every discovery that changes the plan gets recorded wherever the conversation happened to take place, whether that's a pull request thread, a Slack channel, or a comment on a ticket. By release, the document describes what the team intended a few weeks earlier, and the decisions that changed it are spread across tools that nobody will read together again.None of this happens because people are careless. The spec and the work are two separate records, and during implementation, changes are usually recorded wherever the work is already happening. Separate records naturally diverge, and now that <a href="https://atono.io/blog/why-ai-writes-better-code-than-product-specs">coding agents read specifications as instructions</a>, <a href="https://atono.io/learning/product-knowledge-vs-documentation">a stale PRD</a> can produce confident, well-structured code for a version of the product that no longer exists.There's some evidence about what helps. A 2026 study, <a target="_blank" rel="noopener noreferrer" href="https://arxiv.org/abs/2604.05278">Spec Kit Agents: Context-Grounded Agentic Workflows</a>, ran 128 agent runs across 32 tasks in five real repositories. Both arms wrote a specification first; one also grounded the work in the existing codebase and validated the result against it at each step. The grounded runs scored 3.66 against 3.51 on a five-point quality composite, took 13.2 minutes longer inside a 90-minute budget, and moved SWE-bench Lite from 56.5% to 58.2%.Two caveats change what that means. The quality score is a model judging model output, not a human review. And because both arms already wrote specs, the comparison isolates the grounding and validation steps – it says nothing about whether writing a spec helps at all. What it supports is narrower and more useful: the measurable gain came from checking the work against the real system, not from the specification document.

Four things have to be true, and most approaches manage two. The specification stays current as part of doing the work, not through a documentation pass someone has to remember. It's grounded in the system where the work happens, so it reflects the current codebase and the product's own vocabulary. Something checks it against what shipped, so drift gets caught rather than discovered months later. And it carries the decision history, not only the current state, in a form people and agents can both read - knowing why something changed matters as much as knowing what it says now.No approach meets all four equally well, and the differences between them are the useful part of the comparison.<h3>The main approaches</h3>Docs plus integrationsIn this setup, the PRD lives in a documentation tool like Notion, Confluence, or Google Docs, and the work is tracked in Jira. The big advantage is that people across the company can find the document, read it, and comment on it.The connections between these tools have also improved. Notion can pull Jira work items into a page and show the status of linked GitHub pull requests. Atlassian's Rovo Dev can review a pull request and flag whether the code appears to meet the acceptance criteria in the linked Jira work item.What these integrations keep in sync is the linked work, not the document itself. When implementation changes course, someone still has to go back and rewrite the requirements and reasoning in the PRD.Specifications in the repositoryHere, the spec lives alongside the code, usually as Markdown files, architecture decision records (ADRs), or a structured workflow like <a target="_blank" rel="noopener noreferrer" href="https://github.com/github/spec-kit">GitHub Spec Kit</a>. Because the spec is stored with the code, it's versioned and reviewed the same way, and coding agents can read it directly.Spec Kit goes a step further by checking the finished code against the spec and adding tasks for anything that's missing. Teams can also treat the spec as the ongoing source of truth, updating it first whenever the intended behavior changes. That builds the check against implementation right into the workflow.The downside is that people who don't work in the repository, such as product managers, designers, and support teams, have a harder time finding and using the spec. It also doesn't capture what happens after release, like whether anyone used the feature.The work tracker as the specificationSome teams skip the separate document and write the specification directly into their work tracker, such as <a href="https://atono.io/comparison/linear">Linear</a> or Jira. That leaves one fewer record to drift out of sync.Linear has added features that make this approach stronger. Its agent can look at the codebase to understand how the product works, and it can run recurring workflows that keep project documents up to date, while showing which text a person wrote and which came from the agent.How well this keeps a spec aligned with what shipped depends on how the team sets those features up, since they work alongside issues and documents rather than tying them into a single record. If you're weighing Jira for this role, <a href="https://atono.io/comparison/jira">here's how Atono compares</a>.The loop in one systemAtono is a product development platform that keeps the work on one object, the story. Teams plan, build, deploy, and measure on the same <a href="https://atono.io/blog/living-stories-in-action">Living Story</a>, and the context about the product moves with it through each step. Atono's <a href="https://atono.io/learning/what-is-product-knowledge">Product Knowledge</a> provides that context, and AI Context is the part that travels with the work.The <a href="https://atono.io/story-assistant">Story assistant</a> drafts the story using your Glossary, so it starts out reflecting how your product works (more on that in planning with Atono). Coding agents like Cursor, Claude, and Copilot read the story's AI Context before they write code and can record their decisions back to it. Feature flags and usage data live on the same story, so the next round of planning starts from <a href="https://atono.io/blog/what-to-measure-after-you-ship-and-how-to-read-it">how people used what shipped</a>.Atono doesn't automatically check code against the story, so that still depends on your tests, QA and review. Spec Kit's convergence step handles that comparison more directly. See how the pieces fit together.Approaches at a glance<table style="min-width: 100px;"><colgroup><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"></colgroup><tbody><tr><td colspan="1" rowspan="1">Approach</td><td colspan="1" rowspan="1">Examples</td><td colspan="1" rowspan="1">Main strength</td><td colspan="1" rowspan="1">Main trade-off</td></tr><tr><td colspan="1" rowspan="1">Docs plus integrations</td><td colspan="1" rowspan="1">Notion, Confluence, or Google Docs with Jira</td><td colspan="1" rowspan="1">Easy for people across the company to find and use</td><td colspan="1" rowspan="1">Integrations sync linked work, but someone still has to update the document itself</td></tr><tr><td colspan="1" rowspan="1">Specifications in the repository</td><td colspan="1" rowspan="1">Markdown specs, ADRs, GitHub Spec Kit</td><td colspan="1" rowspan="1">Stays close to the code, and tools like Spec Kit can check code against the spec</td><td colspan="1" rowspan="1">Harder to use for people who don't work in the repository</td></tr><tr><td colspan="1" rowspan="1">The work tracker as the specification</td><td colspan="1" rowspan="1">Linear, Jira</td><td colspan="1" rowspan="1">One fewer record to drift, with agent features that can help keep documents current</td><td colspan="1" rowspan="1">Relies on separate agent features working alongside issues and documents</td></tr><tr><td colspan="1" rowspan="1">The loop in one system</td><td colspan="1" rowspan="1">Atono</td><td colspan="1" rowspan="1">Keeps intent, build context, feature flags, and usage on the same story</td><td colspan="1" rowspan="1">Code isn't automatically checked against the story</td></tr></tbody></table>

Every approach moves the synchronization boundary somewhere different, and the practical choice is which kind of drift or friction your team would rather manage.Specifications stored in the repository tend to stay close to the code, but they're less visible to people who don't work there. Specifications stored in documentation tools are easier for the whole company to access, but they're more likely to drift from implementation. Neither problem disappears, so start by asking who reads the spec most and whose mistakes cost more.If engineers and agents are the main readers and the expensive failure is code that misses the requirements, keep specs in the repository and invest in making them readable to everyone else. If product managers and stakeholders are the main readers and the expensive failure is teams building toward different goals, keep the doc and add a step that checks shipped work against it. Trackers and single-system loops try to shrink the trade-off rather than pick a side, so judge them by what checks the spec against what shipped and where the reasoning behind changes ends up.

Before a piece of work is considered done, add one line wherever your spec lives saying what changed from the acceptance criteria and why, or noting that nothing did. If something changed, decide whether the spec or the implementation is now correct, update the other, and record the decision behind it before closing the work. It takes a minute, works in any tool, and after two weeks you'll know how much your specifications actually drift, which is the evidence you need before choosing anything above.

<h3>What is a living PRD?</h3>A living PRD is a product requirements document that stays accurate as the work changes. It stays current as part of doing the work, reflects the current system, gets checked against what shipped, and keeps the history of decisions.<h3>How do you keep requirements in sync with code?</h3>Reduce the number of separate records, ground specifications in the actual codebase, and add a validation step that compares shipped work to the acceptance criteria. Teams do this with specs in the repository, richer work trackers, or a product development platform that keeps planning, release, and usage on the same record.<h3>Should the PRD live in the repo?</h3>It depends on who reads it most. Specs in the repository stay closer to the code but are harder for non-engineers to see, while specs in documentation tools are easier to access but more likely to drift.<h3>What is spec-driven development?</h3>Spec-driven development is a workflow where a structured specification, plan, and task list are written before implementation and used to guide the work, often by AI coding agents. GitHub Spec Kit is a widely used example, and it includes a step that checks the finished code against the spec.

Asset 13   Blog   How Do I Give Cursor or Claude Code Context About Our Product 

There are three common ways to give an agent context about your product: keep instructions alongside the code, connect it to your team's documentation, or connect it directly to the work itself. Each has different benefits and tradeoffs. The bigger challenge is that having the right context available doesn't guarantee the agent will interpret or follow it correctly, so choosing where context lives is only part of the problem.

A 2026 study, <a target="_blank" rel="noopener noreferrer" href="https://arxiv.org/abs/2605.29442">How Coding Agents Fail Their Users</a>, analyzed 20,574 real coding-agent sessions across 1,639 repositories and identified 16,118 episodes where developers visibly pushed back on the agent's work. The sessions included Cursor and Copilot, with CLI logs dominated by Claude-family models. Episodes could receive more than one tag, and three patterns stood out:<ul><li>38.33% were tagged as developer constraint violations. Of those, 73.68% were attributed to instruction-following failures, where the agent failed to follow an explicit instruction from the developer.</li><li>26.95% were tagged as cases where the agent misread what the developer intended.</li><li>22.58% were tagged as inaccurate self-reporting, where the agent said work was complete when it wasn't.</li></ul>Missing context is clearly part of the problem, but these findings complicate the usual advice to simply give AI more context. An agent can have a clear instruction and still not follow it. It can also report that work is complete when it isn't.Most of what follows focuses on where product context should live so an agent can find it. Later, we'll come back to what happens when the agent doesn't follow that context and how to verify what actually happened.

Clearer specifications can help when the problem is missing information. In the study, underspecified instructions were identified as a cause in 15.36% of the validated episodes and accounted for 44.10% of cases where the agent misread developer intent.But instruction-following failures were more common. They were identified as a cause in 36.49% of the episodes, even when the developer had already given the instruction explicitly.So better tickets help, but they only address part of the problem. Teams still need a reliable way to make product context available to both people and AI. In practice, that usually means deciding where that context should live. The next three options take different approaches to that.

The simplest approach is to keep context in markdown checked into the repository, such as CLAUDE.md, Cursor's .cursor/rules/ files, or documentation in a <code>/docs</code> directory. The agent can load it automatically, it's version-controlled next to the code, and an engineer can update it in the same pull request that changes the thing it describes.This shines when the team is small and the context is engineering context. No new tool, no adoption tax, and the guidance sits inches from the code it governs. For one or two repositories and a handful of engineers, it's often genuinely enough. Anything heavier is probably over-engineering.Where it strains is maintenance. Rules can go stale, and keeping them current tends to fall to the shrinking group of people who still understand the system, while everyone else keeps building against whatever is already there. Repo-level guidance also naturally skews toward engineering context. What a product term actually means, why a flow behaves the way it does, or what decision led to a particular constraint often lives with people who don't spend their days in the repository.Even a current, explicit instruction can still be ignored. Making it available doesn't guarantee the agent will follow it.

The next approach is to use the knowledge base where broader product context already lives, including PRDs, decisions, glossaries, user journeys, research, and other documentation in Notion, Google Drive, Confluence, or a similar system. <a href="https://atono.io/blog/mcp-product-context-upstream">Connect the agent through MCP</a>, and it can retrieve that information while it works.This shines because it's cross-functional. PMs, designers, and engineers can all contribute, so the "why" behind a decision lives in human-readable form with history attached. For the narrative side of product context, <a href="https://atono.io/learning/product-knowledge-vs-documentation">a well-kept knowledge base is hard to beat</a>, and most teams already have one.Where it strains is that documentation captures information at a point in time, while product context needs to stay current as the product changes. A stale page can be worse than a missing one because it looks just as authoritative as a current page.The deeper problem is retrieval. The product context is often there, but it's buried inside documents written for people, not machines. A 2,000-word PRD may contain exactly the definition an agent needs, but the agent still has to find the right passage, determine whether it's authoritative, and interpret it correctly. That's why <a href="https://atono.io/learning/ai-context-vs-rag">retrieval isn't the same as structured context</a>.MCP solves access to the document. It doesn't automatically solve which document is right, which part matters, or whether the agent will follow what it finds.

A third approach is to put product meaning closer to the work itself. Instead of storing all of the context in a separate file or prose document, product meaning can live as structured context, including definitions, acceptance criteria, decisions, business rules, and intent attached to the work the team is already doing.That changes the job for the agent. Rather than finding a paragraph somewhere and deciding what it means, it can retrieve the structured object or definition directly. Several products are moving in this direction, though what they expose differs:<ul><li>Productboard gives agents access to product and roadmap context, including features, objectives, releases, and specifications.</li><li><a href="https://atono.io/comparison/linear">Linear</a> gives agents access to planning and delivery context such as issues, projects, and comments.</li><li><a href="https://atono.io/comparison/jira">Jira</a>, through Atlassian's Rovo MCP server, gives agents access to issues and related work across Jira and connected Atlassian tools.</li><li>CoreStory builds a persistent model of a codebase's architecture, workflows, business rules, and APIs from the code itself.</li><li>Reqode links specifications to implementation through a deliberately read-only connection, and after implementation, checks code changes against specifications and architecture guidelines.</li><li>Atono gives agents access to the story itself, its acceptance criteria, <a href="https://atono.io/learning/what-is-ai-context">AI Context</a> notes, and the Product Glossary through <a target="_blank" rel="noopener noreferrer" href="https://docs.atono.io/docs/mcp-server-for-atono">MCP</a>.</li></ul>This shines when <a href="https://atono.io/blog/two-hundred-engineers-two-hundred-ai-setups">several people and AI tools need the same product answer</a> rather than slightly different copies scattered across repositories, documents, and individual conversations. If a product term has a specific meaning, or a permission rule needs to be interpreted consistently, structured context gives the team one place to manage that meaning.Where it strains is that it's another system. Consistency doesn't happen automatically just because the context is structured. Someone still has to own it and keep it aligned with reality, and if ownership is fuzzy, a structured source can drift just like a wiki can, with the added danger that it looks authoritative even when it's wrong.If you're one repo and three people, this is probably more infrastructure than you need, and a simpler rules file may carry you a long way. None of this replaces the agent's judgment either. Better context improves what it has to work from, but it still has to interpret and follow that context correctly.

Some larger organizations build their own retrieval layer instead, using a vector store or internal MCP server connected to the systems they already use. That can be the most flexible option if you have the engineering capacity. You decide what counts as authoritative, how information is structured, which systems are queried, and what the agent can access.The tradeoff is ownership. You now own the infrastructure, the integrations, the retrieval quality, and the job of keeping the source material current.This solves the same problem in a different way, and for some organizations it's the right approach.

Where the context lives answers only part of the problem. In 22.58% of the validated episodes in the coding-agent study, the agent was tagged as inaccurately reporting success. It said work was complete when it wasn't. Checking the implementation itself still matters. Treat an agent's completion message as a claim to verify, not proof that the work is done. Review the changes, run the tests, and check the result against the acceptance criteria.For product work that reaches users, there's another question after that. <a href="https://atono.io/blog/what-to-measure-after-you-ship-and-how-to-read-it">Did the change actually roll out, and is anyone using it?</a> Feature flags and usage data can provide evidence about what happened after implementation.In Atono, the feature flag controlling rollout and the resulting usage data stay connected to the same story as the work. Usage data is available when the application is configured to send usage events to Atono. That isn't a check on the implementation, and it doesn't prevent any kind of agent failure. It gives the team evidence that the work was released, reached users, and – when the right events are instrumented – was actually used.That's a different problem from supplying context, but it belongs in the same workflow. Give the agent the best context you can, check the work yourself, then see what happened after release.

Be honest about where your team actually is:<ul><li>One or two repositories, a few engineers, mostly engineering context → start with repo-level rules. Don't add a tool for a problem you don't have.</li><li>Cross-functional and documentation-heavy, with a knowledge base that's already load-bearing → connecting the agent to that documentation through MCP can get you a long way.</li><li>Multiple teams and AI tools need the same product definitions and structured context → connecting the agent directly to the work starts to earn its keep.</li><li>You have unusual requirements and the engineering capacity to own the infrastructure → building your own context layer may make sense.</li></ul>None of these is inherently more mature than another. They work well in different situations and come with different tradeoffs. <a href="https://atono.io/blog/how-teams-manage-ai-product-context">Pick the lightest system</a> where the answer to "what does this mean?" is the same everywhere it's asked. And whichever one you choose, don't confuse context availability with proof that the agent followed it.

Take a simple example from Atono. "Everything" is the name of a specific screen in the product, not just a common word. Ask an agent to "add a filter to the Everything view" without structured product context, and it has to infer what "Everything" means from the surrounding code. It might build a perfectly reasonable generic catch-all filter that doesn't match the actual product. That's <a href="https://atono.io/learning/why-ai-generates-almost-right-requirements">the kind of situation that produces something almost right</a>.Give it a <a href="https://atono.io/product-glossary">Product Glossary entry</a> that defines Everything as that specific screen, along with the relevant product rules, and the agent no longer has to infer what the term means from the surrounding code. That solves the ambiguity problem, but it doesn't solve the compliance problem. Making the definition reachable doesn't guarantee the agent will interpret every instruction correctly or follow every constraint. And implementation isn't the end of the story either. After the filter is built, the team still needs to verify the implementation and see what happens after release.When the story, rollout controls, and usage data stay connected, the team can answer those questions without relying only on what an agent, an engineer, or a stale document says happened. Checking the work yourself tells you what actually happened.Atono is the product engineering platform – one system that runs planning, building, feature flags and usage data together – that keeps product context connected to the work, so teams and their AI agents build from the same understanding of the product. <a href="https://atono.io/demo">Book a walkthrough with our team</a> to see it. Teams of 25 or fewer can start free, with <a href="https://atono.io/pricing">plans and pricing</a> available as they grow.

<h3>How do I give Claude Code context about my product? </h3>Three common approaches are to keep product instructions alongside the code, connect Claude Code to your documentation through MCP, or connect it directly to the system where the work and its product context live. The right approach depends on where your team already maintains trustworthy context and how many people and tools need the same answer.<h3>Does Cursor read my Jira tickets? </h3>Not by default. Cursor can access Jira data when you connect it through Atlassian's Rovo MCP server or another compatible MCP connection. What it can retrieve or update depends on the connection and permissions you configure.<h3>What's the difference between a rules file and an MCP server? </h3>A rules file such as <a href="http://CLAUDE.md">CLAUDE.md</a> or Cursor's .cursor/rules/ stores instructions with your code so the agent can load them automatically. An MCP server gives the agent a connection to another system so it can retrieve information when needed. Neither guarantees the information itself is current or that the agent will follow it correctly.<h3>Why does AI write code that looks right but isn't? </h3>Sometimes the agent is missing important product context or has to guess what an underspecified request means. But agents can also fail to follow instructions they already have. A large study of real coding-agent sessions found explicit constraint violations were the most common category of observed mismatch between agent behavior and developer intent. Good context reduces ambiguity, but the resulting work still needs to be checked.

Shared context from first decision to feature usage

28 Blog

The right tool doesn’t just help you move fast—it also helps everyone move together. If you’ve been using Linear, you already value speed, focus, and great UX. <a href="/handbook/what-we-believe">Atono was built with those same principles</a>—but designed from the start to support cross-functional collaboration across the full product team.You don’t need to replace Linear to start getting value from Atono. Keep Linear where it works for your team, and use Atono alongside it to bring more product context, structure, and visibility into the work.

<a href="/comparison/linear">Linear</a> is a great fit for development teams to define their work&mdash;but that covers only a small part of the software delivery lifecycle. Product teams work best when they have more context to plan, triage, support, and iterate together. Atono brings structure, visibility, and speed to the entire product workflow&mdash;so your team stays aligned from idea to delivery.

Coordinated delivery with flexible timeboxes Separate time from scope. Atono timelines let teams plan around shared delivery windows without cramming everything into one board or workflow. <a href="/tutorials/advanced-set-up-and-customization/product-themes">Timeboxes handle delivery dates;</a> product themes define scope.
Forecasts based on real delivery speed Each team&rsquo;s backlog shows estimated completion dates based on its historical cycle time. No setup, no guessing&mdash;just a clear forecast based on how your team actually works.
Distinct workflows for stories and bugs No more one-size-fits-all pipelines. Atono separates stories and bugs by design, so features move through a <a href="/tutorials/managing-work-and-prioritization/story-refinement">structured refinement workflow,</a> and <a href="/tutorials/reporting-bugs-and-resolving-issues/bug-triage">bugs get the fast triage</a> they require.
First-class acceptance criteria Acceptance criteria have their own deep links, comments, and attachments&mdash;so they&rsquo;re easy to reference, test, and update. Everything stays connected, even if a story changes.
Feature flags built in <a href="/tutorials/feature-flags-and-rollouts/configure-feature-flags">Flip features on or off </a>directly from the story they were built from. <a href="/blog/how-atonos-feature-flags-empower-fast-development">Atono&rsquo;s built in flags</a> work across environments and log history for easy rollbacks&mdash;no separate tool required.
Bug reports with full context <a href="/tutorials/feature-flags-and-rollouts/atono-chrome-extension-flip-feature-flag">Our Chrome extension</a> captures everything from OS and browser errors to viewport size&mdash;so engineers get what they need to debug, without back-and-forth or external tools.
Feature engagement <a href="/solutions/measure">Keep the entire team informed</a> about customer adoption with a built-in feature analytics&mdash;so key data is visible to everyone, not just the privileged few.

Migrate your issues into Atono with a CSV upload. You can:
<ul>
<li dir="ltr" aria-level="1">
Match teams and product themes by name
</li>
<li dir="ltr" aria-level="1">
Classify items as stories or bugs
</li>
<li dir="ltr" aria-level="1">
Map statuses to workflow steps
</li>
<li dir="ltr" aria-level="1">
Retain original IDs for traceability
</li>
</ul>
<a href="https://docs.atono.io/docs/import-from-linear#/" target="_blank" rel="noopener">Read the import from Linear guide</a>
Try importing a single project to explore how timelines, flags, and structured workflows come together&mdash;and see whether Atono&rsquo;s a better fit for how your team works.

Atono doesn’t require an all-or-nothing switch. Start with the workflows where it adds the most value, keep Linear where it still works, and expand into Atono only when it has earned a larger role in your stack. <a href="/demo">See how it could fit your team</a> in a walkthrough with us.

Plan, build, deploy, and measure in one unified workspace.

AI Blog

AI coding assistance helped developers complete 26% more tasks (<a target="_blank" rel="noopener noreferrer" href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4945566">Cui et al., 2024</a>).Most releases? Still taking just as long.The bottleneck isn’t code. It’s coordination.AI made code writing faster. Coordination didn’t keep up. And when coordination slows, delivery does too.

<a href="/blog/ai-speeding-up-the-wrong-part-of-software">Developers are producing code faster than ever, but delivery speed hasn't caught up</a>. The bottleneck isn't in how fast people code&mdash;it's in how work is prepared, connected, and handed off.
Teams now move faster at the task level but lose time between tasks: in planning debates, duplicate tickets, dependency gaps, and forgotten decisions.
Our philosophy is simple: AI should make coordination lighter, not heavier. That bar shapes everything we build at Atono.

When teams code faster but plan the same way, time savings vanish.
Planning debates replace data-driven decisions. Duplicate bugs clog the backlog and slow triage. Dependencies surface too late. New hires interrupt teammates for context buried in old tickets.
That's the gap: AI accelerated coding, but the work around coding&mdash;planning, connecting, clarifying&mdash;stayed manual.

<a href="/learning/why-ai-generates-almost-right-requirements">Atono's AI actions close that gap</a>&mdash;focusing on small, meaningful moments that make teamwork smoother without adding noise. Every action we build meets four tests:
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Embedded &ndash; Happens where work already occurs, not in a separate tool
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Actionable &ndash; Turns insight into motion you can take right away.
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Explainable &ndash; Shows reasoning so you can trust, adjust, or dismiss it
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Team-level impact &ndash; Improves shared velocity, not just individual output.
</li>
</ul>
If an idea doesn't pass all four, it doesn't ship. Flashy isn't enough.

Atono's AI doesn't rely on generic public models. <a href="/learning/what-is-ai-context">It learns from your team's workspace</a>&mdash;your stories, bugs, and delivery history&mdash;finding patterns in how you plan and ship.
The result: suggestions grounded in your actual work, not assumptions. And because it learns from your data, it gets smarter as your team grows.

See how Atono closes the product-context gap before implementation even starts.

<h3 dir="ltr">Suggested personas</h3>
When you write a user story, <a href="https://docs.atono.io/changelog/april-1-2026-release#use-the-right-personas" target="_blank" rel="noopener">Atono automatically suggests personas</a> drawn from your team&rsquo;s recent work.
Why it counts: It keeps user stories aligned with your team's current product focus. Stories arrive at execution with the right user context, reducing mid-sprint clarifications and rework when personas shift or product direction evolves.
<img src="https://atono-test.directus.app/assets/426bf640-aa42-4366-b461-dd8720a82ac8.gif?key=800w" alt="Persona">
<h3 dir="ltr">Suggested story sizes</h3>
When you write a new story, Atono analyzes the description and <a href="https://docs.atono.io/docs/stories#size-it-for-me" target="_blank" rel="noopener">suggests a likely story size</a> right where you&rsquo;re working.
Why it counts: It replaces estimation guesswork with pattern-based data. Planning meetings that start with data-driven size suggestions run faster&mdash;teams spend less time debating and more time deciding.
<img src="https://atono-test.directus.app/assets/c26372ee-adf4-470c-8762-65832921752f.png?key=800w" alt="Suggested Size">
<h3 dir="ltr">Suggested risk ratings</h3>
During triage, Atono analyzes bug descriptions and proposes probability and impact ratings based on how your team has handled similar issues.
Why it counts: Triage moves faster when priorities are clear from the start with <a href="https://docs.atono.io/docs/bug-triage#set-or-suggest-a-risk-rating" target="_blank" rel="noopener">suggested risk ratings</a>. Critical bugs get attention immediately instead of sitting in the backlog while teams debate risk levels.
<img src="https://atono-test.directus.app/assets/97415f4a-bb1f-44d4-aa16-16a4113c5d89.png?key=800w" alt="Risk Rating">
<h3 dir="ltr">Duplicate bug suggestions</h3>
As you <a href="https://docs.atono.io/docs/bugs" target="_blank" rel="noopener">create or edit a bug</a>, Capy checks your workspace for similar issues.
Why it counts: Duplicates get caught as they're being filed, not during triage. Your backlog stays clean, and your triage team focuses on real prioritization instead of sorting through noise.
<img src="https://atono-test.directus.app/assets/269825b5-cdb0-4155-a923-2320eb81761f.png?key=800w" alt="Duplicate Bug">
<h3 dir="ltr">Suggested items to link</h3>
When you open the <a href="http://docs.atono.io/docs/linked-items" target="_blank" rel="noopener">Linked items</a> section, Atono surfaces a list of stories and bugs it thinks might be related.
Why it counts: Teams discover related work during planning, not during code review. When the checkout team is scoping validation logic, Atono surfaces that the payments team already tackled something similar&mdash;preventing divergent implementations and the refactoring debates that follow.
<img src="https://atono-test.directus.app/assets/993de82f-4de3-43d5-8d48-a9c171e4ae20.png?key=800w" alt="Suggested Links">
<h3 dir="ltr">Ask Capy</h3>
Ask in plain language&mdash;"How do we handle email validation?"&mdash;and <a href="https://docs.atono.io/docs/ask-capy" target="_blank" rel="noopener">Ask Capy</a> searches your shipped stories, synthesizing an answer with source links.
Why it counts: Questions that used to mean interrupting a teammate or digging through old tickets now get answered instantly. Onboarding is faster, decisions are faster, and teams stop re-litigating settled questions.
<img src="https://atono-test.directus.app/assets/a42e7a99-3e80-4b81-a675-2c24d835b3f0.png?key=800w" alt="Capy Answer">
Each of these examples replaces a manual lookup or judgment call with an informed, verifiable suggestion. That's the bar for an AI action: less coordination drag, same human control.

Notice what these actions have in common: they don't accelerate coding. They accelerate the work around coding.
When story sizing is data-driven, planning runs faster. When bug risks are rated automatically, triage focuses on priorities instead of debates. When duplicates are caught early, work doesn't get repeated. When dependencies surface during planning, code reviews and testing move faster.
That's how coordination improvements compound: <a href="/blog/building-above-and-below-the-story">clearer stories</a>, accurate priorities, and connected work mean fewer questions, less rework, and faster execution all the way through the pipeline. Teams spend less time in meetings, less time hunting for context, and less time discovering problems late.
The result: teams that code faster actually ship faster&mdash;because coordination isn't eating the gains.

Atono&rsquo;s approach to AI is intentionally restrained. We don&rsquo;t build for volume; we build for value. Every idea faces one test: will it actually reduce coordination drag?
Auto-summaries nobody reads, <a href="/blog/why-ai-tools-create-coordination-chaos">AI planning tools that create more debate than clarity,</a> smart commit messages developers rewrite&mdash;those never make it past planning.
If it doesn&rsquo;t improve how teams work together, it doesn&rsquo;t ship.

In Atono, AI suggestions appear exactly where decisions happen&mdash;inside the story or bug, at the moment they&rsquo;re needed. That&rsquo;s what makes them actionable: teams can accept, adjust, or dismiss suggestions without switching context or losing focus.
By keeping every interaction in flow, <a href="/blog/how-atono-helps-designers-stay-aligned-with-devs-and-pms">Atono turns AI from a background helper into a natural part of how work moves forward.</a> When AI stays embedded and explainable, teams actually use it&mdash;so productivity compounds instead of fragmenting.&nbsp;
But embedding alone isn't enough. Trust is what makes your teams rely on it.

Atono's AI features are built for reliability, not novelty. We scope every action to a clear, verifiable dataset&mdash;your team's own stories, bugs, and delivery history&mdash;and test each release against accuracy benchmarks before rollout.&nbsp;
No customer data is shared between workspaces, and every suggestion reflects its source context. That's how we keep AI helpful, predictable, and private.
&nbsp;

We built Atono's AI with one principle: if it doesn't make teamwork lighter, it doesn't count.
AI isn't here to replace judgment or creativity. It's here to make good teams unstressed&mdash;removing the coordination clutter that slows them down.
Try Atono's <a href="/pricing">free plan</a> and watch your coordination bottleneck shrink. Create a few stories and watch suggestions appear as you work&mdash;size estimates, duplicate detection, related work connections. As your workspace grows, Ask Capy answers questions from your team's shipped work.
No setup. No training. Just coordination that finally keeps pace with how fast your team codes.
Because when coordination catches up to coding, AI finally delivers what it promised&mdash;faster releases, calmer teams, and measurable results.
AI that earns its name.

Why Modern Teams Are Moving Beyond Jira 

There's a quiet migration happening across software teams. Not a noisy "rip and replace," just a steady stream of teams quietly moving on from Jira.
You hear it in Slack backchannels, hallway chats at conferences, and in demo conversations that start with: "We've used Jira for years, but&hellip;"
These aren't early-stage startups chasing the latest shiny thing. They're experienced engineering organizations that grew up on Jira&mdash;and have decided it's time for something else.
The alternative they're finding? Tools built for how teams actually work today&mdash;where context flows naturally, stories evolve instead of expire, and coordination happens without the tax. But first, let's understand why <a href="/comparison/jira">Jira no longer fits</a>.
This isn't rebellion. It's evolution. Software development has changed, and Jira no longer fits the way modern teams actually work.

When Jira took off, teams were larger, workflows were slower, and formal handoffs were the norm. Its power came from flexibility: custom issue types, complex workflows, and fields for every edge case imaginable. It was built for control at scale&mdash;and for the time, it worked.
But the strengths that made Jira successful twenty years ago are now the very things slowing teams down.
Every company's Jira looks different. Every workflow behaves differently. Every new engineer has to learn your Jira, not Jira itself.
Plugins knit it all together, but they also create fragility.
That complexity isn't a bug&mdash;it's the design. Jira was built for project managers coordinating departments, not for small, cross-functional teams shipping daily. It assumes you'll stop to reconcile the plan.
Modern teams don't pause. They move.

Modern teams don't struggle to decide what to build&mdash;they struggle to stay aligned while they build it.
Teams got smaller and more cross-functional. A PM, a designer, and a few engineers ship features together. Fewer handoffs. More parallel work.
Shipping got faster. Hours, not months. Tools designed for long cycles create drag in short ones.
<a href="/learning/what-is-ai-context">Context became the bottleneck.</a> When you ship weekly, specs can carry you. When you ship daily, context has to live in the work itself.
Stacks exploded. Stories here, flags there, bugs somewhere else, discussion in Slack. Even great tools create friction when they don't share context.
The numbers tell the story: teams lose 30-50% of their time to context reconstruction. A 20-person engineering team can burn an entire engineer's worth of productivity weekly just piecing stories together across tools. That's not a bug&mdash;it's the coordination tax built into disconnected workflows.
That's the real tax: the mental overhead of reconstructing the story every time you switch tools&mdash;the toggle tax.
As one developer wrote on Hacker News, "It's that slowness that makes my soul die a little every time I click." The clicks aren't the killer. The resets are.

Monday: Sprint planning drifts to three hours. You're copying context between Jira, Confluence, Figma, and LaunchDarkly. Everyone leaves with different truths.
Tuesday: A developer needs to flip a feature flag. It's a ticket, a wait, or an out-of-band change that breaks the audit trail.
Wednesday: "What's the status on checkout?" You click through 15 linked tickets, a PR, two specs, and Slack threads to reconstruct one story.
Thursday: A production bug is triaged across Sentry, Slack, and a separate Jira project with a different workflow. It slips two days.
Friday: Seven "quick status" meetings. Same question, different rooms: Where are we on this?
Total weekly overhead: 15+ hours of meetings, 20+ tool switches per developer, 100+ clicks for basic updates.
That's not flow. That's the coordination tax&mdash;charged daily.

The problem isn't just the interface&mdash;it's the mentality it encourages. Over time, Jira can teach teams that progress means moving tickets, not moving the product.
As one engineer put it, "Tickets in Jira aren't the work itself&mdash;they're a performance of the work."
It's easy to start optimizing for visibility rather than velocity, adding fields and steps that make a manager's job easier but slow the team down. The system rewards process over outcomes, control over trust.
And when that happens, people disengage.
Developers delay updates because every interaction feels heavy. Designers stop checking the board because it no longer reflects reality. Managers spend more time reconciling data than leading teams.
Jira didn't just slow teams down&mdash;it changed how they think about work.

Most teams underestimate how much coordination overhead actually costs them.
To understand a single feature, someone might check the Jira epic, open the Confluence spec, review Figma designs, examine GitHub PRs, verify feature flags in LaunchDarkly, and ping someone on Slack for context.
Six tools. One story. That's the problem.
That takes time, sure&mdash;but more importantly, it burns focus. By the time you've pieced the full story together, you've interrupted yourself (and probably someone else).
For a team of twenty engineers, losing just thirty minutes per day to context reconstruction adds up to fifty hours a week&mdash;an entire engineer's worth of productivity lost, every week. That's not a theoretical cost. It's <a href="/blog/true-cost-of-tool-sprawl-consolidation-guide-for-product-leaders">the coordination tax</a> you pay for using disconnected tools.

If Jira's pain points have existed for years, why are so many teams leaving now? Because the world around Jira finally changed faster than Jira itself.
<ul>
<li dir="ltr" aria-level="1">
<a href="/blog/most-popular-jira-alternatives-compared">The alternatives matured.</a> Linear, Notion, and Atono proved that performance and design matter. Teams no longer have to trade power for simplicity.
</li>
<li dir="ltr" aria-level="1">
Expectations shifted. Tools like Figma and Vercel redefined what "good" feels like&mdash;fast, collaborative, context-rich. Jira's interface feels like a relic by comparison.
</li>
<li dir="ltr" aria-level="1">
The toggle tax became measurable. Teams now track time lost to context switching, and the numbers are hard to ignore.
</li>
<li dir="ltr" aria-level="1">
<a href="/blog/why-ai-tools-create-coordination-chaos">AI accelerated the gap.</a> As code generation, testing, and deployment sped up, Jira became the slowest link in the chain. The bottleneck isn't building features anymore&mdash;it's coordinating them.
</li>
</ul>
The tolerance for friction has dropped to zero. When a developer can ship in minutes but it takes five clicks to update status, something feels off.

Teams aren't looking for another issue tracker.
They're looking for a different philosophy&mdash;one that replaces control with connection.
<ul>
<li dir="ltr" aria-level="1">
Integration over configuration. Less setup, more connection. Tools should work together, not depend on plugins to talk.
</li>
<li dir="ltr" aria-level="1">
<a href="/learning/what-is-product-knowledge">Shared context by default. </a>PMs, designers, and developers in the same space&mdash;not translating updates across systems.
</li>
<li dir="ltr" aria-level="1">
Stories that stay alive. Work shouldn't fossilize when the ticket closes. The story should carry lessons, decisions, and outcomes forward.
</li>
<li dir="ltr" aria-level="1">
Workflow intelligence. Tools should surface the right context automatically: flags during rollout, analytics after launch, related bugs during triage.
</li>
</ul>
Here's the shift: Modern software moves fast enough that teams no longer need to be managed. They just need to stay aligned.
Control is dead. Context is king.

So what does "built for modern workflows" actually look like in practice? Not a faster backlog. A different center of gravity.
Atono was designed to fix the problems tools like Jira created&mdash;not by being "Jira but faster," but by rethinking how modern software teams actually work. We built Atono around a different assumption: that coordination should be invisible. That <a href="/blog/living-stories-in-action">stories should evolve, not expire.</a> That tools should help teams stay unstressed, not become another source of stress.
This isn't wishful thinking&mdash;it's how modern tools are built. When stories stay alive (what we call Living Stories at Atono), when feature flags live in context, when workflow intelligence surfaces what you need automatically, coordination becomes invisible. Teams stay aligned without the tax.
<ul>
<li dir="ltr" aria-level="1">
Living Stories sit at the heart of that approach. Each one captures the what, why, and how of a feature&mdash;from concept to rollout to learning&mdash;so context stays connected the entire way through.
</li>
<li dir="ltr" aria-level="1">
<a href="/blog/how-atonos-feature-flags-empower-fast-development">Feature flags live inside stories</a>, so developers can control rollout without leaving context. No separate LaunchDarkly subscription. No filing tickets for permission. No context lost.
</li>
<li dir="ltr" aria-level="1">
Bug reports link back automatically, with diagnostic data collected by the Chrome extension.
</li>
<li dir="ltr" aria-level="1">
Cycle-time tracking reveals where work stalls, and timeboxes turn planning into momentum.
</li>
</ul>
In our own usage, the change was visible in the numbers, not just the vibe. Over six months, our average weekly throughput rose from <a href="https://atono.substack.com/p/how-we-doubled-our-engineering-velocity" target="_blank" rel="noopener">roughly ten to eighteen story points</a>&mdash;while stories stayed the same size.

If you&rsquo;ve been in Jira for years, moving sounds painful. Hundreds of projects, workflows, and users&mdash;it feels like too much to untangle.
That&rsquo;s why Atono&rsquo;s <a href="https://docs.atono.io/docs/import-from-jira" target="_blank" rel="noopener">Jira import</a> was built to lower the barrier, not raise it. It brings over your stories and bugs with their history intact&mdash;team assignments, workflow placement, and key details&mdash;so you can explore Atono with real data instead of demo samples.&nbsp;
You choose how deep to go:
<ul>
<li dir="ltr" aria-level="1">
Light import for stories and bugs only&mdash;great for testing the flow of live work.
</li>
<li dir="ltr" aria-level="1">
Include users involved in those issues to preserve assignees and comment authors.
</li>
<li dir="ltr" aria-level="1">
Include full teams if you&rsquo;re ready to bring everyone.
</li>
</ul>
Every imported item keeps its original creator, status, and timestamps. Teams land right in their Atono workflows, so you can pick up where you left off&mdash;without rebuilding from scratch.
Start where it makes sense&mdash;one project, a few, or all of them.
Keep Jira read-only as a safety net while you see how it feels to plan and ship in flow. You&rsquo;ll know quickly if the difference is real.

And yes&mdash;some teams are fine on Jira. If you ship monthly, rely on heavy compliance workflows, and your updates live in slide decks anyway, you might be okay.
But if you're shipping continuously and losing hours to context reconstruction, you're paying a tax you don't need to pay.
Software development changed. Teams are smaller, cycles are shorter, and context is everything. Jira was built for a world that prized control. Today's teams need flow&mdash;and trust.
The question isn't whether the shift is happening. It's when your team will make it.
If you&rsquo;re ready to stop paying the coordination tax, <a href="/demo">book a walkthrough with our team</a> and see how Atono supports the way modern product teams actually work.

Atono Blog Dev

Real feedback from our team on what actually makes a difference.
We recently shared how switching to Atono <a href="https://open.substack.com/pub/atono/p/how-we-doubled-our-engineering-velocity?r=3okggi&amp;utm_campaign=post&amp;utm_medium=web&amp;showWelcomeOnShare=false" target="_blank" rel="noopener noreferrer">doubled our engineering velocity</a>. That post focused on team-level changes &mdash; clearer ownership, better refinement, and smaller, more consistent stories.
But just as important is what changed day to day.
The improvements didn&rsquo;t come from working longer hours or adding more process. They came from reducing friction &mdash; creating an environment where people can focus longer, switch tasks more fluidly, and recover context quickly when interruptions happen.
Each change on its own is small. But together, they create an environment that helps developers stay in flow.

Most tools flood teams with notifications across email, in-app inboxes, and chat. Over time, that noise makes it harder to tell what actually matters.
Atono takes a quieter approach. There&rsquo;s <a href="https://atono.substack.com/p/why-were-not-implementing-another" target="_blank" rel="noopener noreferrer">no built-in inbox </a>and no constant stream of nudges. Updates only surface when they&rsquo;re genuinely useful, and they show up in channels teams already rely on.
For us, that&rsquo;s Slack. We&rsquo;ll get a message when something meaningful changes &mdash; like a feature flag being enabled in a specific environment or a Slack channel being connected to a story. These are updates that help teams stay aligned without pulling people out of flow.
And if something stalls, Atono doesn&rsquo;t escalate loudly. It simply marks stories and bugs as stale, making it easier for someone to follow up when it actually matters.
Fewer interruptions. More trust in your tools.
<img src="https://atono-test.directus.app/assets/4d2e07f8-e79b-4e5d-b19d-877f471079dd.jpg?width=2410&amp;height=1144" alt="Staleness Indicator">

Staying in flow is easier when the work you&rsquo;re responsible for &mdash; and the work you care about &mdash; is easy to find.
Atono&rsquo;s Home page starts with a simple default view that shows the stories and bugs you&rsquo;re accountable for right now. You can also follow work that matters to you, even if you&rsquo;re not the owner &mdash; so important stories stay visible without manual check-ins or status pings.
From there, it&rsquo;s easy to click into more detail or move into the Everything view to scan broader context. Saved views make this flexible. Teams can create views for things like bugs ready to start, recently updated stories, or work that&rsquo;s taking longer than expected. Those views can also be turned into widgets, so the Home page reflects what matters most to each person or team.
For those who prefer working directly in the backlog, filters and layout options make it easy to narrow focus without losing visibility into the bigger picture.
The goal isn&rsquo;t to prescribe how people work &mdash; it&rsquo;s to keep relevant work close, visible, and easy to access.
<img src="https://atono-test.directus.app/assets/8b8a83b9-351a-4091-a88c-d8025cf2d277.jpg?width=2386&amp;height=1692" alt="Dashboard Widgets">

Nothing breaks flow faster than hunting for context.
When implementation is underway, switching between tools just to find a flag name, a Slack conversation, or a related bug adds friction that compounds quickly.
In Atono, that <a href="/blog/living-stories-in-action">context lives with the story</a>. Bugs are linked directly, Slack channels are attached, and feature flag configurations are visible by environment. It&rsquo;s easy to copy a flag name, toggle it for testing, or jump into the right conversation without losing momentum.
Acceptance criteria live alongside the story too, with direct links that make it easy to reference or share specific requirements during implementation or review.
By pulling this context into one place, Atono helps teams focus on building rather than stitching information together.
<img src="https://atono-test.directus.app/assets/ad217df6-d90e-41c3-91ac-d2f8e8262d7b.jpg?width=2422&amp;height=1476" alt="Story Context">

Flow doesn&rsquo;t always mean staying on one task for hours. Often, it means being able to shift between related work without losing your place.
Atono makes those transitions lighter. Filters and saved views help surface what you need quickly. Drafts stay open if you step away and come back later. Navigation is fast, and views don&rsquo;t reset every time you move around.
That makes it easier to review something you touched earlier, check on a related bug, or start shaping a new story &mdash; without having to reload context each time.
It&rsquo;s not about eliminating interruptions. It&rsquo;s about making recovery easier.
<img src="https://atono-test.directus.app/assets/b5dc2012-2143-445a-8022-6b75e919d83f.jpg?width=3020&amp;height=1470" alt="Draft View">

You shouldn&rsquo;t need perfect recall to find your work.
Atono&rsquo;s search supports partial matches, recent activity, and even small typos, making it easier to surface the right story or bug. Results show where a match appears &mdash; in the title, description, acceptance criteria, or comments &mdash; so you can quickly scan for what&rsquo;s relevant.
When you&rsquo;re not sure what you&rsquo;re looking for, <a href="https://docs.atono.io/docs/ask-capy" target="_blank" rel="noopener">Ask Capy</a> helps bridge the gap. You can ask questions in plain language &mdash; like &ldquo;what changed in checkout last sprint?&rdquo; or &ldquo;which bugs are still open for enterprise customers?&rdquo; &mdash; and get answers grounded in your actual stories and bugs.
Filters layer on top of that, letting teams narrow by assignee, workflow step, team, and more. Useful combinations can be saved and reused from the Everything view.
Less time hunting. More time working with momentum.
<img src="https://atono-test.directus.app/assets/7c46745b-f3ae-4dc1-9d49-e7a28932581e.jpg?width=2268&amp;height=1348" alt="Search">

Atono didn&rsquo;t increase velocity by asking developers to move faster. It helped by shaping an environment that supports focus, preserves context, and reduces unnecessary friction.
For teams that care about modern tooling and developer experience, these details add up. Staying in flow isn&rsquo;t about heroics &mdash; it&rsquo;s about removing small obstacles that quietly drain attention and energy.
This is what that looks like in practice.
If you&rsquo;re curious how this kind of setup plays out on a real team, we&rsquo;re always happy to walk through <a href="/solutions/engineers">how our developers use Atono</a> day to day.

Solutions

Resources

Karla

Senior Communications Specialist

Articles by Karla

Tools for a living PRD that stays in sync with the code

How do I give Cursor or Claude Code context about our product?

How Atono works alongside Linear

AI actions explained: what counts and why

Why modern teams are moving beyond Jira

5 ways Atono helps you stay in flow as a developer