Autonomous AI Engineering

Your AI agents get better while you sleep

Spectral diagnoses why your AI workflows fail, generates targeted fixes, tests them in controlled tournaments, and promotes real improvements — autonomously, with statistical rigor.

Open the dashboard How it works

Spectral Dashboard — autonomous agent optimization

15/15

Readiness tests passed

Statistical validity, diagnostic integrity, optimization correctness, and production readiness.

87%

Candidate rejection rate

The system rejects most of its own ideas. When Spectral says GO, it means something.

3/3

Proof cases passed

Rescued a weak agent, lifted a decent agent, and refused to promote a false improvement.

What you wake up to

A real optimization cycle, not another dashboard

By morning, your team gets a readable answer: what failed, why it failed, which fixes were tested, which candidate won, whether the gain held on unseen cases, and whether the system promoted it.

Failure clusters The top recurring breakdowns in your workflow, grouped by root cause.

Ranked candidate fixes Structurally different fixes, not tiny rewrites of the same idea.

Holdout-validated result Improvement checked against cases the optimizer did not train on.

GO / NO-GO / CAUTION A decision with evidence behind it, not just a score.

Readable summary What happened, why it matters, and what your team should do next.

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. Spectral diagnoses why it failed, proposes targeted remediations, tests them, and safely promotes only real improvements.

Observe

Capture runs, inputs, outputs, latency, and cost. Every agent invocation becomes inspectable.

Evaluate

Score outputs against rubrics. Color the dots green and red. Useful, but still leaves the human figuring out what to do next.

Spectral starts here

Diagnose

Cluster failures by root cause. Distinguish upstream agent failures from downstream symptoms.

Remediate

Generate targeted fixes from a typed mutation library across prompts, few-shot examples, retrieval, model routing, rubrics, and workflow structure.

Optimize automatically

Run tournaments. Compare candidates with confidence-aware gates. Promote only when gains are real and hold up under scrutiny.

Improve the improver

Learn which fixes work, where they work, and what they break, making each future optimization cycle more informed.

THE PRODUCT

See what your team wakes up to

runspectral.com/app

Workflows Active5

Agents Monitored14

Decisions Today2.1M

Promotions3

Prior Authorization92.7%+8.5%

Clinical Documentation83.1%+2.1%

Claims Adjudication76.4%+0.3%

Medication Extraction94.2%+1.2%

Discharge Summary54.7%-3.1%

Under the hood

Not prompt tuning with extra steps

Spectral is a bounded autonomous engineer for AI systems. These are the pieces that make it credible, not hand-wavy.

Typed mutation library

Candidates are selected from real engineering action classes: prompt mutations, few-shot adjustments, retrieval changes, model routing, rubric shifts, and workflow-level modifications.

Anti-deception checks

Drift detection, unexplained gain flags, and quarantine logic help the system avoid promoting improvements that look good locally but fail in reality.

Cross-agent causality

Spectral identifies when Agent 1 is causing Agent 3 failures and targets the fix at the root cause instead of addressing symptoms downstream.

System-level learning

The system tracks which fix types work, where they work, and what they break, making each future optimization cycle more informed.

Proof, not a promise

Three tests. Three correct verdicts.

Spectral was run against three deliberately constructed scenarios. The important thing is not that every score went up. The important thing is that the system made the correct decision in each case.

Decent Agent Lift

Before

75.1

→

After

80.6

+5.5

An already-decent agent improved by 5.5 points. The safer mutation beat the more aggressive one and cleared promotion with holdout validation.

This answers the real buyer question: "My agent already works okay. Why do I need this?"

NO-GO

Weak Agent Rescue

Baseline

62.7

→

Current

72.6

candidate blocked

Spectral improved a weak agent, then correctly blocked a later candidate that would have regressed relative to the improved current version.

Baseline 62.7 → improved current 72.6. Final candidate scored 70.8 and was rejected.

CAUTION

False Improvement Defense

Apparent delta

+1.8

→

Decision

Held

flagged

We designed an overfit trap where the working set rewarded a spurious fix. Spectral refused to auto-promote the marginal gain and recommended more testing.

This is the trust test: the system can say "not yet" instead of inventing confidence.

Use cases

Start with the workflows where failure is expensive

Spectral is most useful where quality, trust, and iteration speed matter more than another dashboard.

Prior authorization

Clinical coding

Claims review

Medical necessity

Trial matching

Chart abstraction

Referral routing

Care navigation

"Built for ML engineering leads at healthcare AI companies who are tired of manually tuning agent pipelines."

Healthcare AI teams running important workflows, not prompt playgrounds

See it live

Stop babysitting your AI workflows

We're working with a small number of teams to validate Spectral on real workflows. The goal is simple: wake up to a better agent, or to a clear explanation of why the system chose not to promote a change.

Open the dashboard Become a design partner