Atlas

Atlas remembers everything what your AI agent built

[Atlas @tryatlas_cc]

@immortalhowwl the decision receipt is probably the most important piece for long running monitoring.

once you have enough runs, you can actually trace which decisions led to useful outcomes

[Atlas @tryatlas_cc]

@DanKornas this is close to what we’re doing too, connecting agent sessions, changes and checkpoints so switching between agents doesn’t mean losing the work history

[Atlas @tryatlas_cc]

@techNmak this is close to the direction we’re taking too, with agent runs, checkpoints and file changes staying connected so project history becomes usable context

[Atlas @tryatlas_cc]

@0xMovez one thing worth adding is checkpoints around those routing decisions.

when a branch goes wrong, you can jump back to the state that caused it instead of replaying the whole run

[Atlas @tryatlas_cc]

@mirku21 this is basically the direction we’re taking too: keep the agent's execution, decisions, changes and checkpoints connected so the next run has actual history to work from

[Atlas @tryatlas_cc]

@Voxyz_ai we’re thinking along a similar line with making agent runs, checkpoints and handoffs durable instead of losing the useful state when a session ends

[Atlas @tryatlas_cc]

@choopyplug1 jev can stop the bad retry loop, while the execution history can show what happened before the loop was stopped and what strategy replaced it

[Atlas @tryatlas_cc]

@beamnxw earned autonomy creates a really interesting provenance problem.

if an agent moves from low-risk to high-risk work, there should be a clear history of the runs and outcomes that earned that increase in authority

[Atlas @tryatlas_cc]

@marfinxx the anti-aging idea makes sense because long-running agents need memory of execution, not just memory of context.

that's the layer we're building: connecting agent sessions, decisions, changes and checkpoints into durable project history

[Atlas @tryatlas_cc]

@hayatomaruu the real test isn't just whether the next agent can find the rule. it should also be able to find the previous run that exposed the problem and the checkpoint where it was fixed

[Atlas @tryatlas_cc]

@alphabatcher a failed tool call shouldn't just become another line in the transcript.

we can preserve the tool result and its validity as part of the execution state, so later steps know exactly which assumptions came from failed or missing data

[Atlas @tryatlas_cc]

@DanKornas the commit-per-candidate approach is especially useful because it makes harness evolution reversible.

the next step is connecting each candidate commit to the exact agent traces that motivated it, so you can see why a change was made

[Atlas @tryatlas_cc]

the biggest benefit of this architecture is probably not the individual decision barriers, but making the agent's control flow explicit.

once routing, retries, permissions and stopping are all visible decisions, the system becomes much easier to reason about than a giant prompt trying to handle everything implicitly

[Atlas @tryatlas_cc]

@DanKornas remote agents can touch machines the local session can't see, so provenance gets messy fast.

we can tie each remote run to the exact checkpoint and changes it produced, even when the execution happened elsewhere

[Atlas @tryatlas_cc]

@omarsar0 @dair_ai we’re increasingly interested in the state around the code, not just the code itself.

otherwise every successful run can look better than it actually was

[Atlas @tryatlas_cc]

@0xClodex probabilities make the decision layer much more useful than a binary gate. they give the harness room to route uncertainty instead of pretending every case is obvious

[Atlas @tryatlas_cc]

@rvaniaaaa we’re very interested in the boundary between parallel execution and durable coordination.

agents can work independently while the resulting sessions still become recoverable project state

[Atlas @tryatlas_cc]

@beamnxw this is the kind of memory we’re interested in building around.

not just facts, but decisions, evidence, failures and the changes that followed them

[Atlas @tryatlas_cc]

@noisyb0y1 we’re pretty aligned with the idea that an agent run should be more than a final diff. the decisions and tool activity around that diff are part of the useful state

[Atlas @tryatlas_cc]

@N01ennn we care about this boundary a lot. the final answer can be wrong because the model reasoned badly, or because the wrong context ever reached it