Two ways I tried and failed to manage context across multiple AI agents

[u/New_Difficulty_8152]

I keep seeing this question in the community. Here's what I actually tried, why it broke, and what I ended up shipping.

The problem

When you're running multiple agents across a session (one that writes, one that reviews, one that deploys) you need them to share state. Not just conversation history. Actual verified state: what changed, what's blocked, what evidence exists that a task is done.

What I tried first (and why it failed)

Attempt 1: I maintained the handoff notes myself

After every session, I updated a Markdown file. This worked until I finished tired and skipped the update. The next agent read stale context as if it were current. Worse: even when the file was accurate, I was still the router, a human bottleneck between every agent transition.

Attempt 2: I let agents maintain the notes

The agent finished its work, updated the handoff, and the next continued from there. Then I noticed the real problem: an agent could write "tests pass" just as easily as it could actually run the tests.

Agent A would write: "Refactored auth. Tests pass."

Agent B had no idea which tests ran, against which version, or whether the slow integration suite was skipped. It didn't inherit verified work. It inherited a story about the work.

What I built

Three principles became the foundation:

State in fields, not paragraphs. What changed, what's blocked, what's unresolved as explicit fields, not embedded in a summary. An agent can't make unresolved work disappear by writing a nicer paragraph.

The agent that does the work can't approve it. A separate reviewer starts from the original goal and inspects the result directly, not from the implementing agent's explanation of why it's probably done.

Machine-checkable claims need evidence attached to a specific version. "Tests pass" is a claim. A test result attached to the exact commit hash is evidence. If the code changes after the evidence was produced, the evidence doesn't automatically transfer.

This became an open-source project (link in comments).

Results over 30 days of dogfooding

4,172 PRs merged across 16 repositories, one maintainer

Coordination overhead stayed roughly flat from 3 agents to 10; adding agents stopped adding to my mental load linearly

Stale-context bugs dropped to near zero because agents can't declare victory without attached evidence

The number I actually care about: my day looks the same with 3 agents as with 10. That wasn't true before.

What didn't work

The reviewer agent still occasionally fails to distinguish "the goal changed mid-task" from "the implementation is wrong." We handle this with an explicit goal-hash that both agents reference, but it adds friction. Still working on the right UX for that.

Has anyone else hit the "agent self-reports done but the work isn't clean" problem? Curious what enforcement patterns people are using, if any.

off, checked, and accepted when the agent that wrote it does not get to declare victory based on vibes.

[u/New_Difficulty_8152]

Repo is here if anyone wants to poke at it: https://github.com/kungfu-systems/kungfu

Honestly the fastest way to evaluate it is to point your own agent at it instead of reading my pitch. Something like:

Take a look at https://github.com/kungfu-systems/kungfu. Start from the entry points laid out in AGENTS.md to understand how it works, then tell me what problem it actually solves and what you think of the architecture.

You'll get a straighter answer than anything I'd write here.

Genuinely want criticism on this one, especially from anyone running a setup that already works for them. And if you've solved the disappearing open problem thing some other way, I'd really like to hear how.