Your AI is downstream. The fix is upstream.

You ask Cursor to refactor part of your billing system. It reads the surrounding code, makes the changes, runs the tests, and everything passes.

A week later, support flags a problem. Nothing is technically broken, but the behavior is wrong for your product. The issue isn’t that the AI failed to understand the codebase. It’s that “billing” means something specific inside your company, and that context didn’t exist in the story the AI was working from. The workflows, assumptions, constraints, and edge cases behind the feature simply weren’t there.

That's a hard category of failure, because it doesn't announce itself. Obviously broken output gets caught. Almost-right output gets shipped, and surfaces a week later when someone uses it.

Most attempts to close the context gap focus on the coding session itself. Teams add repo instructions, maintain cursor.md or CLAUDE.md files, wire internal docs into MCP, build prompt templates, or expand retrieval so the model has more context available while it’s coding. And in many cases it isn’t even addressed by the team - each engineer creates their own solution to the problem resulting in further drift and duplicate effort.

Those approaches help, but over time we started noticing the same pattern: By the time an engineer or AI coding assistant is working from a story, the important product framing has already happened.

If the story itself doesn’t carry enough product understanding, the implementation layer ends up reconstructing context after the fact. That changed how we started thinking about AI tooling for product teams.

Inside Atono, the AI workflow starts during story authoring, not just implementation. A PM, tech lead, or product owner works with the Story Assistant to draft stories, clarify the problem being solved, and refine acceptance criteria before implementation starts.

The Story Assistant draws from the workspace Glossary, which is a structured collection of the product’s key concepts: feature names, workflows, screens, roles, and domain language. Each concept includes a plain-English description, related concepts, and alternate terminology the team already uses internally. The Glossary gets built from existing documentation and evolves alongside the product over time.

That matters because a lot of product understanding never gets written down cleanly. Teams know what certain terms mean internally, which workflows connect, which assumptions are safe, where edge cases tend to appear. But those details rarely survive the trip from someone’s head into a story.

As stories get refined, Design Decisions get captured alongside the work itself: what got scoped out, which assumptions were safe, the workflows that mattered, the deferred edge cases.

When we ran our Glossary on Atono’s own work, around 60% of AI-generated stories needed meaningful corrections. That reinforced something we were already starting to suspect – without the upstream loop, AI authoring drifts quickly, and structuring context during authoring is what catches that drift before the story reaches engineering.

When the story reaches engineering, the product context is attached – terminology correct, decisions captured, edge cases noted. Which leaves one question: how does that story get from the Atono product to the AI tool the engineer is actually using?  

If you’ve been heads down for the last year, MCP is the open standard that allows AI tools to interact with external systems and structured context. Anthropic open-sourced it in late 2024, it moved to the Linux Foundation in 2025, and at this point most major IDEs and AI tools support it in some form. Cursor, Claude Code, Copilot, Windsurf, Zed, JetBrains, Gemini, ChatGPT.

MCP matters because it gives AI tools a consistent way to access context without relying on copy-paste workflows or one-off integrations. It is the wire, not the answer. What matters is what flows through it: the context your AI is actually receiving when it starts work on a story.

When Cursor or Claude Code connects to the Atono MCP server and fetches a story, it’s not just pulling a ticket title and some acceptance criteria.

The AI gets the working context around the story: the story itself, its workflow state, its attachments, and its AI Context – three sections, automatically read when the AI fetches the item:

• Design Decisions – product and scope decisions made when the story was authored. Tradeoffs considered, acceptance criteria framing, scope boundaries, what got explicitly deferred.

• Technical Investigation – research findings, implementation constraints, prior discoveries, architectural considerations, and open questions related to the work.

• Technical Changes – the running record of implementation changes already made on this story.

The story carries its own history forward instead of forcing engineers to reconstruct it from Slack threads, docs, and memory. By capturing Design Decisions during scoping and utilizing the Glossary throughout the process, the authoring loop ensures that the story reaching the developer tool is deeply informed by product context, rather than just being an isolated record of its own history.

Product context enters once, where it's cheapest to get right. It travels with the work.

Couldn't I just stay on Jira and bolt this onto Cursor?

Two honest answers, depending on what you're asking.

If the question is your product terminology – the Glossary is portable. Build it from your product docs, export it as a TSV file, and drop it into your AI tool's project context as a rules file, project memory, or however your tool consumes structured context. Your AI tools get shared product concepts that otherwise live only in people's heads, regardless of where your tickets live. Any team, any stack, today.

If the question is bigger – you've got a team that wants the full loop, but the org isn't migrating off Jira – that's a common situation, and it's the one the “vanguard motion” is for. A vanguard team adopts Atono for the work they're doing: Story Assistant authoring, Design Decisions captured on the story, AI Context flowing to their dev tools through MCP. Overall planning, the cross-team roadmap, and the squads that aren't piloting stay in Jira. The vanguard team proves the loop on real work; the rest of the org isn't disrupted. We support imports from Jira and Linear with external IDs preserved, so the vanguard team can pull in the work they're picking up.

Most teams that go this route do it in sequence: Glossary first, a vanguard team on the full loop second, broader decisions later.

If you're trying to make Cursor or Claude Code understand your product right now, your product knowledge is probably scattered. Some of it is in repo instructions. Some in prompt templates or rules files. Some in a markdown doc someone wrote several months ago. Most of it isn’t written down at all. Every written copy drifts on its own, and there's no single version because nobody owns one.

Rules files and prompt templates don't go away with Atono – they still handle code style, conventions, output format, tool preferences. That isn't product context, and Atono doesn't replace it. What changes is the product-context part specifically: terminology, decisions, the concepts behind the work. The Glossary holds the terminology in one place, with defined owners and permissions. Stories carry the specifics – scope, decisions, edge cases – as AI Context. The product-context portion stops being something every engineer keeps a private, drifting copy of.

When the Glossary lives in Atono – whether you're consuming it as a TSV today or through stories and MCP on the full loop – product terminology gets managed in one place, used during story authoring, and carried forward into implementation. It can be managed like the critical asset that it is, and everyone, and every AI tool, reads from the same version.

Two things get better when product context is structured this way. The day-to-day experience of working with these tools: less scaffolding to maintain, less context to reconstruct mid-session. And the quality of what they produce: fewer almost-right stories reaching engineering, and fewer problems surfacing a week after ship.

If you want to see the upstream loop end to end – Story Assistant authoring through to the MCP handoff in Cursor – we'll walk you through a demo.

If you'd rather start with the Glossary, build it from your docs and export the TSV. It works with any AI tool, on any stack, and it's the lowest-friction way to see whether structured product context changes your AI output. MCP server docs are at docs.atono.io/docs/mcp-server-for-atono.