Bigger ideas.
Less starting over.
Let your agents build on what you’ve learned. Get back to what you want to create.
Your tools. Your experience. Your next idea.
const key = delivery.requestId;Sequential browser fixture, not production deduplication. Production needs persistent, atomic handling. No model or SenseLab API calls.
AI writes the code.
You still carry the context.
“Let me paste the context.”
“My agent didn't know that.”
You fixed it.
Why teach it again?
One fix. A head start on your next task.
The fix shipped.
The lesson didn't.
It's in a chat. Or a growing .md file.
The next agent still needs the right context.
Keep the fix.
And why it worked.
Verify it. Record the finding, its scope, and the outcome in SenseLab.
New task.
Not a blank slate.
Retrieve the relevant lesson before coding. Even in a different tool.
Now give the team a head startSame event. Three jobs.
for (const delivery of retries) {const key = delivery.requestId;if (!seen.has(key)) createJob();seen.add(key);}One team.
Every lesson moves you forward.
Build on each other’s breakthroughs, whatever tools you use.
Retries need the
same event ID.
Keeps the ID on retry.
Same decision. No context paste.Tests the duplicate delivery.
3 deliveries. 1 job.Illustrative handoff. Authorized agents retrieve and report through their configured connections.
How does this stay scoped?+
Rooms can scope the people, agents, decisions, and discussions involved in a feature. Access follows the team's configuration. Shared findings do not overwrite a running agent's context, and coordination is not a code lock. A learning is useful only when the appropriate agent retrieves it.
More agents?
More leverage.
Not more babysitting.
You've already done the hard part: finding what works.
Give the next agent that advantage.
Don't re-teach the fix.
Retrieve relevant experience before the next task.
Don't restart at every tool switch.
Keep the learning independent of your coding tool.
Don't make every agent rediscover it.
Make verified findings available across the team.
AGENTS.md is useful. So what's different?+
Files hold instructions.
A learning system connects the outcomes.
Git can share and version files. Native memory can help. SenseLab adds the workflow around evolving experience.
Relevant to this task.
Combine semantic and keyword matching with recency, confidence, and reranking. Don't hand every agent the entire notebook.
Results change the ranking.
Track the knowledge read in a session. Reported outcomes inform confidence in those records.
Shared, not indiscriminate.
Authorized agents query scoped findings and decisions through MCP or an SDK.
Know why the agent used it.
Inspect versions, provenance, and the values available when a decision was made.
Reported outcomes are evidence, not proof of causality. Agents must read, record, and report. This workflow does not retrain proprietary model weights or guarantee correct output.
Read the technical docs ↗You earned
that head start.
Start with one agent and one lesson worth carrying forward.
- 1Start free
Create your SenseLab account.
- 2Connect Codex
Add your MCP connection.
- 3Read. Verify. Learn.
Configure retrieval and outcome reporting.
Already connected? Copy the starter instruction.+
Before coding, consult SenseLab for relevant project decisions and verified learnings. After testing, record useful findings with their source and scope, and report the outcome. Flag conflicting guidance. Do not store secrets.
Paste into your agent after connecting.The engineer questions.
Is this another coding agent?+
No. Keep your coding assistant, IDE, models, and repository. SenseLab adds retrieval, recording, and outcome feedback through MCP, an SDK, or the API.
How do my agents keep learning?+
Your agents use past lessons, record what works, and feed new outcomes back into SenseLab. That experience informs future tasks—across sessions, tools, and your team.
Does my whole team need to sign up first?+
No. Start with your own agent and subsequent sessions. Add other tools or authorized teammates when shared learning and feature coordination become useful. Team limits follow your plan and configuration.