Every session makes your agents smarter.
The Learning Engine watches every agent run, distills what worked and what didn't, and feeds those lessons forward - so the system gets better with every commit, without manual prompt tuning.
Your team's institutional knowledge, compounding.
First-pass success rates climb as the Learning Engine absorbs your codebase, your reviews, and your conventions. Illustrative curve from the CodeCourier internal eval set.
Your team's institutional knowledge, encoded automatically - no manual prompt tuning, no model fine-tuning.
The learning loop, in four moves.
An offline pipeline that turns every agent run - successful, failed, abandoned - into durable, reusable context for the next one.
- 01
Capture
Every agent action, decision, tool call, and outcome is recorded in structured event streams - the raw substrate for everything that follows.
- 02
Distill
An offline distillation pipeline analyzes successful and failed runs to identify recurring patterns, common pitfalls, and team conventions.
- 03
Aggregate
Raw learnings are synthesized into reusable rules, attached to repos and personas, versioned like code so you can review, diff, and roll back.
- 04
Inject
New agent runs bootstrap with prior learnings as durable context - no cold start, no relearning the same lessons, no wasted tokens.
Six kinds of knowledge, captured automatically.
The Learning Engine doesn't try to model everything. It looks for the patterns that move first-pass success - the ones senior engineers carry in their heads.
Code conventions
Your team's style, naming, idioms, and the patterns reviewers reach for - encoded automatically.
Common pitfalls
Bugs your codebase repeatedly produces, caught once and avoided in every subsequent run.
Architecture decisions
ADRs, module boundaries, naming hierarchies - the structural rules new code has to respect.
Review feedback
Recurring critique patterns from human reviewers, turned into pre-emptive guardrails for the next PR.
Domain knowledge
Business rules, terminology, and edge cases that aren't in the code but matter for the right answer.
Tool preferences
Which libraries, frameworks, and idioms your team picks - and the ones they actively avoid.
Measurable lift, run after run.
Measured on the CodeCourier internal eval set, Q1 2026. Illustrative - real lift depends on your codebase, review culture, and run volume.
You decide what gets learned, and where it lives.
Learning is opt-in at every layer. Nothing leaves a repo unless you explicitly promote it, and every aggregation lands with an audit trail.
Repo-scoped
Learnings stay within their repo by default. A pattern captured in one project never leaks into another without an explicit promotion.
Persona-scoped
Tag specific learnings to specific agent roles - the TypeScript specialist's lessons stay separate from the docs writer's.
Org-scoped (opt-in)
Cross-repo aggregations are available for platform teams when you want shared conventions to travel together.
You can review, pin, or reject any aggregated learning. Nothing ships without an audit trail - and every injection is replayable.
Powers the rest of the product.
The Learning Engine isn't a standalone surface - it's the layer that makes every other CodeCourier capability quietly better over time.
Ready to watch your team's expertise compound?
Deploy your first agent in minutes. Every run from that point on feeds the Learning Engine - and every subsequent run reaps the lift.
Hire your first AI engineer.
Ship by lunchtime.
5 minutes to onboard. First PR within an hour. Cancel anytime.