Autonomous Quality Engineering for AI Agents

Your AI agents get better while you sleep.

Upload your agent traces. Spectral finds cross-agent failure patterns no human would catch, generates targeted fixes, and validates every change on data the optimizer never saw. Set a budget and walk away.

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Spectral Dashboard — autonomous agent optimization

+19.9 points

on a healthcare prior auth pipeline

64.2 → 84.1 across 7 validated promotions. Fully autonomous.

14.5x cascade lift

found in under 10 seconds for $0

The agent that looks broken isn't the one that needs fixing. The failure tensor sees what trace logs can't.

0 regressions shipped

across 230+ autonomous scans

Every improvement validated on holdout data the optimizer never saw. Statistical rigor, not vibes.

What you're doing right now

A $500K/yr process that doesn't scale

Engineer stares at traces in a spreadsheet

Guessing which prompt change will help. No systematic way to find the root cause.

They ship it. It breaks something else.

The symptom shows up in Agent C, but the root cause is in Agent A. Nobody catches it for a week.

Eval says 95%. Customers say "the bot is wrong."

Both are true. Your eval is measuring the wrong thing. No rubric calibration, no correlation to real outcomes.

Repeat. Hope for the best.

Manual review. Manual prompt changes. Manual regression testing. No validation against unseen data. No way to know if it actually worked.

There's a reason your agents plateau at "good enough."

The maturity gap

Most tools stop at Stage 2. Spectral starts at Stage 3.

Your observability stack shows what happened. Your eval tool scores it. Then what? A human reads the scores, guesses at a fix, and hopes it works.

Observe

Capture runs, inputs, outputs, latency, cost. You already have this.

Evaluate

Score outputs against rubrics. Color the dots green and red. You probably have this too. It tells you what's bad. It doesn't tell you why or what to do about it.

Spectral starts here

Diagnose

Cluster failures by root cause. Trace them across agent boundaries. Tag each cluster: prompt deficiency, retrieval quality, input data, or infrastructure.

"Your resolution agent produces wrong timelines because your evidence agent skipped the merchant remedy check. The fix is two agents upstream from where the symptom appears."

Remediate

Generate targeted fixes from 21 mutation types across 3 risk tiers: surgical insertions, restraint mutations, chain-of-thought scaffolds, validation checklists, downstream adaptations. Each candidate targets a specific failure cluster.

"Three structurally different fixes for the same failure pattern. Not rewrites — interventions."

Optimize

Run tournaments. Compare candidates head-to-head with confidence-aware gates. Validate on holdout data the optimizer never saw. Block every change that regresses any dimension.

"14 candidates tested. 13 blocked — each would have made the pipeline worse. 1 promoted."

Know when to stop

When the bottleneck isn't prompts — it's data quality, retrieval, or infrastructure — Spectral says so. That diagnosis saves weeks of wasted prompt engineering.

"Remaining failures are retrieval quality (40%) and input data issues (25%). Fix the data pipeline, not the prompts."

Under the hood

Statistical rigor, not vibes

Every improvement is validated on cases the optimizer never saw. The system blocks ~7 candidates for every 1 it promotes.

6 scales

Not everything is a prompt problem

Model config, output format, agent topology, tool injection, context management. Six diagnostic scales try cheap fixes ($0) before expensive ones. 91% hit rate on format issues via schema enforcement vs 9% via prompt rewriting.

4 providers

Right model for each agent

GPT-4.1 for analysis. Gemini Flash for parsing. Grok for real-time research. Sonnet for complex reasoning. Each agent gets the optimal model — not one-size-fits-all.

Cascade analysis

The broken agent isn't the problem

Cross-agent failure tracing finds the upstream root cause. 14.5x cascade lift: fix one agent's output format and two downstream agents improve. Other tools score individual traces — they can't see this.

Anchor validation

Every promotion verified on holdout

Human-verified gold-standard cases silently validate every promotion. If the optimizer gaming its own eval, the anchor set catches it and rolls back the change. Trust the numbers.

Proof, not a promise

Your agents have failure patterns right now that you don't know about.

We've proven it across three industries. Same engine. Same result: the visible failure is never where the root cause is.

HEALTHCARE

Prior Authorization

Baseline

64.2

→

Final

84.1

+19.9

7 validated promotions across 14 scans. CPT-code lookup, few-shot examples, rubric reweighting, model routing, chain-of-thought verification. The tensor found a 14.5x cascade lift that no human reviewing traces would have caught.

7 validated promotions. Zero PHI exposure (HIPAA-compliant sanitization).

CODE REVIEW

PR Review Pipeline

Genesis V0

80.6

→

Final

89.9

+9.3

Created from one sentence: "Review pull requests for a Python codebase." Genesis built 5 agents with 10 task-specific rubrics. Autonomous optimization with dimension-targeted mutations that improved quality without regressing code analysis accuracy.

Fully unattended. Set a budget and walk away.

COMPLIANCE

Zero PII Exposure

PII Items Tested

→

Redacted

100%

SSNs, dates, medical codes, financial amounts, phone, email, NPI, account numbers — all redacted before reaching any external LLM. Real theory generation ran on sanitized data. Full audit trail preserved.

Two-pass sanitization: regex + local SLM. HIPAA-ready. Zero data exfiltration.

These are our benchmarks. The question is what Spectral finds on yours.

Get started

Your agents get better while you sleep.

Upload your traces to diagnose an existing pipeline. Spectral finds improvements no human would find, executes them without breaking what works, and validates every change on holdout data. Free first scan. Results in minutes.

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Free · $500/mo Growth · $2,000/mo Production · Enterprise custom

Built by

Shane Fitch

President at Documo — $1M → $10M+ ARR Head of Growth at Altva

Spent 5 years scaling Documo from $1M to $10M+ ARR as President. Started deploying AI agents and hit the same wall every team hits — observability tools show what’s broken, but nobody closes the loop. Built Spectral to be the autonomous quality engineer that diagnoses, fixes, and validates multi-agent pipelines without human intervention.

Book a call LinkedIn shane@runspectral.com