Background Agents: Letting Your Team’s Knowledge Write Itself

What if your team didn’t need to remember to document decisions, client calls, or key chat threads — because an agent quietly did it for you?
The rise of background agents is quietly reshaping how teams build and sustain knowledge.

In the daily flow of work, the richest insights often happen in places we don’t track: a Slack thread about a design change, a PR comment explaining architectural reasoning, or a quick call where someone outlines “how we’ll do this for the next client”. Too often these vanish—buried in chat, un-documented, lost to onboarding and history.

Enter background agents: software that listens to these signals, digests them, and writes your knowledge base for you.
Products like Cursor support agents that run in the background of developer workflows. Others, like Devin, position cloud agents as “team-members” themselves. Tools such as Granola already automate meeting-note taking for teams. These examples reflect a broader idea: the shift from people logging knowledge to agents capturing it.

Why team knowledge systems need this

• Friction kills adoption: A system that relies on humans to “remember to update the wiki” often fails. Automatic capture reduces that toil.
• Versioning and consistency: When an agent writes or updates the document, it can attach metadata, show provenance, and keep a clean history—so you’re sure the “right version” is surfaced.
• Unified workspace, less fragmentation: Instead of separate pipelines (Slack → doc, PR → wiki, meeting → notes), imagine one system that handles many signals.
• True knowledge continuity: Onboarding goes faster. Lost context becomes found. When someone leaves, their decisions aren’t lost.

Modern research supports this direction: for example, a study on chat-platform agents found that embedding knowledge capture into conversation tools improved document quality and revision history retention. arXiv

How a Slack integration would work

Here’s how you could see this working:

1. Integrate with Slack: The agent monitors predefined channels (e.g., #client-decisions, #arch-review) or threaded conversations flagged by keywords or user ‘decision’ emoji.
2. Capture and draft: When a signal is captured, the agent drafts a document: summarising key points, capturing the participants, linking the thread, and identifying action items.
3. Review & accept: That draft lands in your central knowledge hub as a new entry. You get a Slack DM: “Draft created from thread #design-changes: review & accept?”
4. Publish or revise: If you accept (or edit), the document is published and versioned; if you reject, it remains editable or gets archived.

Over time, your team's workspace becomes a living archive of decisions, fixes, onboarding logs, client docs — all authored automatically, curated collaboratively.

This subtle integration means your team doesn't need to switch tools or remember to file anything. The workspace becomes the source of truth.

Beyond retrieval: the “perfect RAG” thought experiment

In AI research, there’s a concept of a retrieval-augmented generation (RAG) system that can fetch perfect evidence every time, and then feed an LLM to generate flawless answers. Imagine a perfect RAG: zero gaps, 100% accuracy. It’s powerful for personal knowledge—your brain’s polyfill for memory.

The challenge: for teams, knowledge isn’t just one person retrieving—it’s shared context, governance, edit control, onboarding. A perfect agent that writes knowledge is fantastic—but unless it lands in a discoverable, editable, versioned hub, you still face silos, drift, or loss when people leave.

That’s why the value lies not just in the agent capturing things, but in the workspace that hosts and organises them: structured, safe, and team-accessible.

Key design choices (and tradeoffs)

• Human in the loop vs full automation: You probably don’t want the agent to publish without review. Inline edits and accept/reject flows are crucial.
• Scope of capture: Which signals matter? All messages? Only tagged threads? Narrowing to high-value channels avoids noise.
• Governance & trust: Agents need permissions, auditing, and opt-in transparency: who captured what, when, from where?
• Structure & schema: If the agent writes everything into an unstructured blob, discoverability suffers. Having consistent tags, categories, links matters.
• Privacy: Some channels are private or client-specific. The system must respect what gets captured and where it gets published.

Why this matters for small teams

Small teams often skip documentation because it feels like extra work. Yet the cost of onboarding, context switching, or repeated decisions adds up fast. A background agent flips the script: documentation happens as you work. You don’t write it after; it writes while you do.

The effect? Less time lost to confusion. More institutional memory. Faster ramp-ups for new hires. Less fear of “what happens when Jane leaves and we lose her Slack threads”.

Final word

Background agents are no longer a far-off concept—they’re already capturing meetings, chat threads, code decisions and turning them into searchable knowledge. But the real advantage happens when that agent output lives in a central, versioned, team-accessible workspace. When you pair the agent and the hub, your knowledge system transitions from manual logs to live, evolving history.

If your team uses Slack and you’re tired of “we’ll document that later”, consider letting the agent document it now. The knowledge doesn’t wait. Neither should you.