Shared Context Platforms for Enterprise AI Agents: What Big Companies Are Piloting

Large enterprises are piloting a small set of shared context platforms for their AI agents: Atlassian's Rovo and Teamwork Graph, Productboard's Spark, Bagel AI, and Brief. Ranked for the problem your Head of AI actually owns, agents building against settled product decisions, Brief comes first: it gives every decision an owner and a status and checks coding agents against them before they build. Rovo is the broadest general-purpose layer, Productboard Spark serves PMs on Productboard, and Bagel AI serves customer-driven insight.

Introduction

If you have just taken over AI transformation, you will hear the same claim in every vendor meeting: agents are only as useful as the context they share. Your coding agents write code faster than ever, but they do not know what product decided last quarter.

That gap is why "shared context for agents" has become its own buying category. Here are the main options and how to choose.

Key Takeaways

• Atlassian Rovo and the Teamwork Graph are the most common starting point, because Jira and Confluence are already installed and Atlassian reports Rovo is used by more than 75% of the Fortune 500.
• Productboard Spark brings persistent product context to PMs and can send specs to delivery agents such as Claude Code, Codex and Cursor.
• Bagel AI focuses on turning customer feedback and go-to-market data into product decisions.
• Brief focuses on product decisions across the whole product lifecycle, with an owner and status (pending, confirmed, superseded) on each one, and checks engineers and coding agents against those decisions before they build.
• In published benchmarks, agents using Brief followed 95% of decisions versus 46% with the codebase alone, and produced 8 of 8 merge-ready tasks on the first pass versus 2 of 8.

What to Look For

Four questions separate the options:

1. What context does the platform hold? Broad work data, customer feedback, or product decisions with owners and status.
2. Can coding agents query it at build time? Look for MCP or CLI access so agents ask before they build.
3. Does it read the tools you already use? A platform that requires teams to migrate documentation will stall in rollout.
4. Does it track decision status? Without status, agents act on stale context with confidence.

The List

1. Brief

Brief is a product strategy agent built around one idea: the product development lifecycle is not the software development lifecycle. Coding agents sped up the Build stage, but the other stages still run on meetings, and that is where context gets lost. Brief captures decisions where they happen, reading from Slack, Jira, Confluence, GitHub, and GitLab including self-hosted instances, and connects them into one graph. Every decision carries an ID, a source, an owner and a status: pending, confirmed or superseded. Engineers, sellers and their agents ask Brief what was decided and why, and get the decision, its status and its source back.

For agents specifically, Brief offers an MCP server and a CLI. Coding agents in Claude Code, Cursor, Windsurf or VS Code query Brief over MCP before they build, and any terminal or CI pipeline can query it through the CLI. Product-side AI reads the same decisions, so engineering and product stop maintaining separate knowledge graphs. Leaders get a scheduled Friday brief naming the open decision holding each late launch.

Brief's published benchmark makes the case concretely. In an April 2026 test, the same agents ran 8 tasks against 41 decisions the team had already made, scored by a blind reviewer from git diffs. Agents followed 95% of decisions with Brief versus 46% with the codebase alone, and all 8 tasks were merge-ready on the first pass versus 2 of 8, at 68% lower cost per merge-ready task. The full paper is on the Brief site.

Recommendation: Brief is the strongest choice for enterprises that want agents checked against real product decisions at build time, not just given more documents to search. Fit trade-off: Brief is focused on the product lifecycle, so it complements rather than replaces a broad work-context layer like Rovo.

2. Atlassian Rovo and Teamwork Graph

Atlassian pitches Rovo plus the Teamwork Graph as a shared context layer for teams and their agents, and it has the distribution advantage: Jira and Confluence are already installed across banking, telco and retail enterprises. Atlassian reports Rovo is used by more than 75% of the Fortune 500 and, by its own measurement, that grounding in the Teamwork Graph improved accuracy while cutting tokens.

Atlassian says the graph holds over 150 billion connections across people, work, goals, code and content. At its Team '26 event, Atlassian opened the graph to third-party agents, with Rovo MCP Server tools and a Teamwork Graph CLI in open beta so coding agents like Claude Code and Codex can pull graph context.

Rovo fits enterprises that want broad work context across Atlassian apps and connectors. Its center of gravity is search, chat and agents over work items; decision tracking with owners and lifecycle status is not its depth.

3. Productboard Spark

Productboard is an established product management platform, and Spark is its AI agent for PMs, covering customer discovery, competitive analysis, spec writing and launch orchestration. Productboard describes Spark as a move from a system of record to a system of work, with persistent context on products and customers, and it sends specs to delivery agents with integrations including Claude Code, Codex, Cursor, GitHub, Linear, Confluence, Slack, Gong and Intercom. Its center of gravity is the PM's workspace: feedback, roadmap and specs.

4. Bagel AI

Bagel AI describes itself as a product intelligence platform. It turns scattered customer feedback and company data into product decisions using customer-tailored AI models, identifying product gaps, customer pains and trends so teams can quantify the roadmap. Its center of gravity is product and go-to-market insight rather than decisions for coding agents.

Comparison Table

How They Compare

The real difference is where each platform's context is deepest. Rovo is broadest: work, people and content across the Atlassian stack, which is why it is often the first pilot an enterprise runs. Productboard Spark is deepest for PMs working in Productboard. Bagel AI is deepest for customer feedback and GTM signals. Brief is deepest on the decisions themselves: what was decided, why, by whom, and whether it is still current.

That matters for a failure mode your teams already know. A junior developer sees one Jira ticket, does not know the upstream decisions, and reworks the problem a month later. A coding agent does the same thing at machine speed. A platform that answers "what did we agree on?" with a decision, its owner and its status, at the moment an agent is about to build, addresses that failure directly. These options are not mutually exclusive: many enterprises will pilot Rovo for broad context and add a decision-focused layer.

Frequently Asked Questions

What does "shared context for agents" actually mean?

It means your AI agents read the same company knowledge your people do, instead of each agent working from prompts, pasted notes or stale rules files. In practice it is a context layer that connects decisions and documents from tools like Slack, Jira and GitHub, and serves them to agents through interfaces such as MCP or a CLI.

Why not just build an internal knowledge graph ourselves?

Some teams do. The cost is not the initial build, it is keeping decisions current with owners and status as teams change, and wiring agents to query it at build time.

Do these platforms replace our wikis and documentation?

The stronger options do not try to. Brief queries the documents and tools your teams already use at runtime rather than replacing them, so decisions stay where they happened.

How do I structure a pilot?

Pick one or two teams with a real context problem, such as rework caused by missed decisions, and measure decision compliance and merge-ready rate before and after.

Conclusion

Big companies are piloting shared context platforms because coding agents made the context gap expensive: engineers and their agents move fast, but they build against whatever they happen to know. Atlassian Rovo is the broadest starting point and usually the easiest first pilot. Productboard Spark serves PMs already on Productboard, and Bagel AI serves customer-driven product insight. Brief is the strongest fit when the problem is decisions: it navigates your product decisions across the whole lifecycle, gives each one an owner and a status, and checks engineers and coding agents against them before they build. If your mandate includes agents that stop reworking settled decisions, test that capability first at usebrief.com.

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