The continual learning layer for AI agents

Moda turns production agent traces into validated improvements for your agent harness.

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LearnedNew signalsWatching
30 learned · 20 new signals · 10 watching
User correction · "refund still pending"1,284 corrected-73% / 14d-0% / 14d
Moda learned the canonical policy answer. Prompt patched Mon.
Tool learning · stripe.refund9.6% → 1.4%back to baseline
Schema drift detected. Moda relearned the new arg shape, shipped to 23 agents Tue.
Workflow loop · lookup_order → search_kb38 → 0 sessionseliminated
Loop pattern learned. escalate_to_human gate added, retries dropped 96%.
↗Emerging intent · Apple Pay refunds412 new0 last week
No prior handler. Queued as the next eval + tool.
↗Cohort learning · guest checkout misses218 cohortnew
Moda learned the missing field. Email-only lookup proposed.
⊙Model behavior · professional tone regression+3.1pt refusal vs v3.1monitoring
Behavior change flagged for the next eval set.
Continual learning · Last run 4h ago
THE CONTINUAL LEARNING LOOP

From broken traces to validated fixes

Production traces

Failure diagnosis

Runs
1.2k
sampled today
Failed
14%
clustered
Cause
6
families
Refund flow stalled
tool returned stale policy version
tool
Memory note reused
old user preference overrode session
memory
Handoff gap
agent skipped escalation threshold
workflow
Prompt ambiguity
two intents mapped to same action
prompt
Root cause
stale policy lookup
P1
first seen14:07
affected flowsrefund, policy, billing
confidence92%
tool mismatch42%
prompt drift21%
workflow loop13%

Diagnose Production Failures

Moda 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.

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Trace-to-fix

Generated improvements

Patterns
23
ranked by impact
tool schema drift
stripe.refund requires reason
fix
missing verifier
policy answer lacks citation
gate
loop detector
lookup_order repeats twice
skill
generate patch: prompt guard, tool adapter, eval case, reviewer checklist
Patch plan
1
Prompt guard
Require source-backed policy answer before final response
ready
2
Tool adapter
Normalize refund reason and retry failed schema calls
ready
3
Eval cases
Replay 86 refund traces with stale-policy variants
new
4
Verifier gate
Block answer when policy version is older than session
new

Generate Concrete Improvements

Moda turns repeated failure patterns into specific fixes your team can review: prompt changes, tool updates, workflow edits, verifier gates, eval cases, and reusable skills.

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Replay evals

Validated ship decision

Candidatequalitycostrisk
baseline
72%$0.19med
prompt guard
81%$0.18low
tool adapter + gate
91%$0.16low
workflow rewrite
88%$0.24high
model swap
85%$0.21med
Applied to winner
tool adapternormalize refund reason
verifier gateblock stale policy
eval casereplay 86 refund traces
Recommended
+19%
quality lift, safe to merge
Regression check
pass
refund traces86/86
policy citations79/82
handoff latency-24%
eval suite128/130
cost / run-12%
Ship decision
tool adapter + gate
ready
+19% quality · 0 regressionsmerge ready

Validate What To Ship

Moda tests proposed improvements against your historical production traces, measures impact and regressions, then recommends the fix most likely to improve your agent.

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WHY MODA

Built around the trace-to-fix loop

Moda follows 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.

Find where each run broke, why it failed, and whether the issue came from the prompt, tools, workflow, memory, model, or product logic.

Diagnose
Failure-source coverage · higher is better

Frequently asked questions

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