Automatically fix and optimizeyour agent’sWorkflow.

MEGA Code automatically turns measured failures into verified improvements to prompts, tools, orchestration, and code, compounding across your team, org, and fleet.

Request early access See how it works

The problem

Finding a failed run is only the beginning.

An agent can complete without an error and still fail the job. Once that run is flagged, you still have to connect related failures, trace them to the code, decide what to fix first, and check the change against previously passing cases.

Outcome failureExecution completed

The run completed. The job didn't.

Required instructions were missed

A required tool was skipped or misused

The answer was incomplete, irrelevant, or ungrounded

Manual follow-through

The failure was found. The manual work just started.

Group related failures

Map problem and code dependencies

Decide what to fix first

Verify the change and regression impact

The solution

From scattered failures to evidence-backed decisions.

MEGA Loop doesn’t replace your observability stack. It uses your existing traces to diagnose, generate, and verify the fixes.

01Human input

Import your traces

Import selected traces from:

LangfuseLangSmithArize Phoenix

MEGA LOOP

Diagnose · fix · verify

Group similar failures

Cluster the failed cases to reveal repeated problem groups that need deeper analysis.

Analyze dependencies

Prioritize the bug fix list based on related code or dependencies.

Verify the candidate fix

Compare current vs. candidate versions across failed cases, regression set, quality, cost, and response time.

Prepare the draft PR

Package the passing change with its diagnosis and proof of fix in a review-ready draft PR.

06Human review

Review and decide what ships

Review the evidence and code, then decide whether to merge and release. Nothing ships without your approval.

MEGA Loop · bug graphProduct preview

Full graphGroup list

Search bugs...

Filter

Sort: severity

Sequential = top-down · Parallel = same row · Independent = separate

Auth / session

Sequential

Bug 1

Bug 4

Bug 5

Payments

Sequential

Bug 20

Bug 21

Bug 23

Concurrency / format

Parallel

Bug 6

Bug 7

Routing

Sequential

Bug 12

Bug 13

MEGA Advantage: Dependency-awareness

Many failures can share one cause. Fix that first.

Treating every trace as a separate bug can lead to redundant fixes. MEGA Loop maps dependencies across failure groups, trace steps, and related code so that you know what to fix first and what the changes may affect.

Prioritize your fixes

Prioritize shared upstream problems before downstream symptoms.

See the impact

Understand the change scope and which related code may be affected.

MEGA Advantage: Evidence-gated fixes

MEGA doesn’t suggest a fix without proof

MEGA Loop doesn’t replace your observability stack. It uses your existing traces to diagnose, generate, and verify the fixes.

MEGA Loop · PR detailProduct preview

span_error:compare_months

Ready for review019ed9c5Open PR

Status · Verified · draft PR prepared·Linked bug · tool-call failure

Latest commitCommit history

Spot fix

applied

fileapp/tools.pysymbolcompare_monthssummaryTreat missing totals as 0.0 before calculating the delta.

app/tools.py+1 −1

− delta = this_total - prev_total

+ delta = (0.0 if this_total is None else this_total) - (0.0 if prev_total is None else prev_total)

Counterfactual check

verified · fail → pass

TypeError · unsupported operand type

Re-ran the failing span · no longer raises

Gating

passed · no regression detected

regression sample · 25 of 382 spans · 6.5%

Re-ran 25 previously-passing spans · all passed

Draft PR

draft

[MEGA Loop] verified fix for linked failure group

branchautodebug-group-tool-call → mainlabelsauto-fixneeds-review

Open PR

Draft · review and merge it yourself · no auto-merge

After debugging: Agent optimization

Every run is smarter than the last.

Optimization is a continuous cycle that truly enables self-evolving agents. Each iteration compounds on the last and the system gets better with each run.

Epoch Start01

Sample seed set

A stable subset becomes the target every iteration has to beat.

Iter 002

Baseline measurement

Score the current pipeline. Every future iteration is judged against this number.

Iter 103

Wisdom curation

The graph assembles a curated orchestration — not a retrieval dump.

Iter 1 → N04

Iterative refinement

Execute, validate, refine. Each pass compounds on the fixed subset.

Epoch Boundary05

Validation test

Confirm gains generalize to unseen data before closing the epoch.

Loop Continues06

New seed sampled

The Wisdom Graph carries everything learned into the next epoch.

MEGA Loop early access

Be among the first to turn failed runs into verified fixes.

Join the early access waitlist. We’ll reach out as pre-launch access becomes available.

Pre-launch access