Your agents should stop
relearning how you work.
Fleet Lessons turns corrections your existing AI harness already understands into governed, versioned policy. You decide what sticks, where it applies, and when it changes.
Use the harness you already have through MCP or REST. Fleet runs no refinement model and requires no Fleet chat.
Observed pattern
Lesson proposal
“Always run the race detector before merging authentication changes.”
- Proposed by
- Workload via MCP
- Evidence
- 3 independent tasks
- Scope
- Project
- Target
- House rule
Exact effect preview
add_requirement · auth.merge_checks · race
Your harness learns. Fleet makes the learning durable.
The harness already has the conversation, model, and task context. It recognizes the correction and submits a complete proposal. Fleet takes over at the governance boundary.
Your harness understands
Use the model already in the room
The harness sees the correction, compares relevant candidates, and writes the complete Lesson proposal with the model already serving the task.
Fleet governs
Turn meaning into controlled change
Fleet validates identity, scope, evidence, lifecycle, target authority, revisions, and the exact change that would be applied.
People decide
Keep authority human
An authorized person reviews the exact preview. Workloads can propose and apply approved changes, but they can never approve them.
From correction to policy, without a second AI system
Your harness supplies the intelligence. Fleet supplies the rails.
Correct
Tell your existing harness how you want the work done.
Propose
The harness submits a strict, complete Lesson through MCP or REST.
Review
Fleet checks authority, scope, evidence, conflicts, and the exact target preview.
Approve
A person approves or rejects that exact revision. Servers cannot borrow human authority.
Apply
Fleet applies direct targets or governs harness adoption, records provenance, detects drift, and can reverse it later.
One correction, only where it belongs
A preference should not accidentally become company policy. Every Lesson names an authorized scope, and broader promotion is a separate human decision.
- 1
Personal
Private policy for one person.
- 2
Agent type
Shared behavior for a stable kind of agent.
- 3
Team or role
Policy for one operating group.
- 4
Project
Repository-specific policy.
- 5
Organization
Company-wide policy, explicitly promoted.
Full-scope learning, not a notes folder
A Lesson governs the real artifact that controls behavior. Fleet supports the complete target model instead of trimming every correction down to a personal preference.
Personal policy
Private instructions composed before inference.
House rules
Managed requirements and prohibitions scoped across the organization.
Agent prompts
Versioned changes to managed prompt sections.
Workflows
Exact, validated patches to saved workflow definitions.
Repository prompts
Governed changes through an approved pull request.
Skills
Tenant-owned skill changes with immutable source and adoption proof.
Keep the intelligence where it already is
Fleet does not discover, refine, embed, or semantically deduplicate Lessons. Your harness does the semantic work with the model you already chose.
The same governed lifecycle over MCP and REST.
No Fleet Chat dependency.
No transcript or chain-of-thought field in the contract.
Optional evidence excerpts are bounded and controlled separately.
Automation can carry policy. It cannot grant itself authority.
Fleet recognizes exactly two identities, with a hard boundary between automation and human decisions.
Interactive identity
OAuth identifies the person using the dashboard, CLI, or MCP. Personal scope and approval stay attached to that person.
Server identity
Fleet-issued client credentials identify servers and unattended harnesses. They can propose and apply approved changes.
No borrowed authority
A server cannot inherit a person’s scopes. Approval and promotion always return to an authorized human.
Tenant master switch
Per-person opt-out
Observed-pattern control
Evidence excerpts off by default
Every Lesson has a lifecycle—and an undo button
Learning should compound without turning into invisible, irreversible policy.
Exact previews
Approval binds to one revision and the exact rendered target—not a vague summary of intent.
Versioned history
Optimistic revisions and immutable provenance prevent silent rebasing or invisible mutation.
Drift detection
Fleet can distinguish an adopted Lesson from a target that changed afterward.
Explicit reversal
Reject, supersede, retire, or revert without pretending the old policy never existed.
Fleet Lessons, in brief
Is Fleet reading and analyzing every chat?+
No. The harness already serving the task recognizes and refines the correction, then submits a strict Lesson proposal. Fleet has no transcript or chain-of-thought field. Optional evidence excerpts are separately controlled and bounded.
Do I need Fleet Chat?+
No. Lessons is designed for the harness you already use. Fleet provides the governance and lifecycle layer through MCP and REST.
Does Lessons work with Codex, Claude Code, and OpenCode?+
Yes. The hosted Lessons API can be used by Codex, Claude Code, OpenCode, another MCP client, or a direct REST client. Fleet’s coding-agent runner uses Claude Code or OpenCode, including models those harnesses support such as Claude, Codex, and Grok.
Can a server approve a Lesson?+
No. Fleet recognizes human and workload identities. Workloads can propose and apply already-approved changes, but approval and promotion require an authorized human.
What can a Lesson change?+
Personal policy, house rules, managed agent prompts, saved workflows, repository prompts, and tenant-owned skills. Each target has its own validation, authority, preview, application, drift, and reversal contract.
Can we turn learning off?+
Yes. Administrators have a tenant master switch and separate controls for observed patterns, personal Lessons, and evidence excerpts. Each person can narrow those settings further for their own identity.
What happens when a Lesson is wrong?+
It can be revised or rejected before approval, superseded or retired later, and reverted after adoption. Fleet retains the lifecycle and provenance so the reversal is explicit and auditable.
Let corrections compound.
Keep the harness your team already uses. Add the governance layer that turns what it learns into policy you can review, apply, and reverse.