Intent F1
0.82 → 0.94
Lock 20 · adaptlab.click
The continual learning layer for AI agents
From broken traces to validated fixes. Adaptlab turns production agent traces into validated improvements for your agent harness — without retraining the model.
Summit pound · lock-keeper cabin
Learned · New signals · Watching
Friction
14.2 → 3.1%
Frustration
6.8 → 2.4%
Illustrative product UI · not customer averages. 3 learned · 2 new signals · 1 watching.
Three chambers
From broken traces to validated fixes
The same loop in production: diagnose where agent runs break, generate concrete harness improvements, and validate candidate fixes against historical traces before your team ships.
Chamber 1 · Failure diagnosis
Diagnose Production Failures
Adaptlab analyzes your agent logs to find where each run broke, why it failed, and whether the issue came from the prompt, tools, workflow, memory, model, or product logic.
Refund flow stalledtool
Memory note reusedmemory
Handoff gapworkflow
Prompt ambiguityprompt
Root cause · stale policy lookupP1 · 92%
Chamber 2 · Generated improvements
Generate Concrete Improvements
Adaptlab turns repeated failure patterns into specific fixes your team can review: prompt changes, tool updates, workflow edits, verifier gates, eval cases, and reusable skills.
1 Prompt guard · source-backed policyready
2 Tool adapter · normalize refund reasonready
3 Eval cases · replay 86 refund tracesnew
4 Verifier gate · block stale policynew
Chamber 3 · Validated ship decision
Validate What To Ship
Adaptlab tests proposed improvements against your historical production traces, measures impact and regressions, then recommends the fix most likely to improve your agent.
Candidatequalitycostrisk
baseline72%$0.19med
prompt guard81%$0.18low
tool adapter + gate91%$0.16low
workflow rewrite88%$0.24high
Recommended +19% quality · 0 regressionsship
Why Adaptlab
Built around the trace-to-fix loop
Production conversations in. Continually improving agents out. Gates open only when a candidate is validated — the model weights stay put.
Diagnose
Find the break
Where each run broke, why it failed, and whether the issue came from the prompt, tools, workflow, memory, model, or product logic.
Generate
Write the patch
Reviewable prompt changes, tool updates, workflow edits, verifier gates, eval cases, and reusable skills — not a full pretrain.
Validate
Open the gate
Replay proposed improvements against historical traces, measure impact and regressions, then recommend what is safe to merge.
Standing orders
Frequently asked questions
What is the continual learning layer for AI agents?
A layer that turns production traces into validated harness improvements. Diagnose where runs broke, generate concrete prompt/tool/workflow patches, and validate candidates against historical traces before your team ships.
How does Adaptlab improve my agent without retraining the model?
Prompt and tool patches, workflow edits, verifier gates, eval cases, and reusable skills. Inference plus an eval loop — not weight-level fine-tunes or a full pretrain.
What signals does Adaptlab surface from production conversations?
User corrections, tool error rates, workflow loops, emerging intents, cohort misses, model behavior regressions, friction and frustration rates. Example tiles in the cabin are illustrative product UI, not customer averages.
How is Adaptlab different from a tracing tool?
Tracing records. This layer proposes and validates what to ship: prompt guards, tool adapters, eval cases, and reviewer checklists. The gate stays closed until a candidate is merge-ready.
How does frustration root cause attribution work?
Friction and frustration rates are tied to a suggested patch. Root causes cluster by family — tool, memory, workflow, prompt — with a confidence on the cause.
What LLM providers and frameworks does Adaptlab support?
The product sits above the harness you already run. The public extract does not list a vendor matrix; write ceo@adaptlab.click with the stack you use.
How long does setup take?
Write ceo@adaptlab.click to book a demo. We will not invent a timed SLA that is not Adaptlab’s to claim.
Is this a full retrain?
No. Continual learning without retraining. Every trace should make your agents better; the model weights stay put.
Summit pound
The learning loop your agents need.
Production conversations in. Continually improving agents out. Every trace should make your agents better.