Agent Optimization
LLM agents don't stay optimized after deployment. Model providers push silent updates. Prompt sensitivity drifts as context shifts. Data distributions change. Most teams discover these failures through customer complaints or compliance incidents, both of which cost far more than the systematic testing that would have caught them.
Agent optimization is the discipline of continuously measuring and improving agent behavior in production, not just at launch. TestML brings that rigor to enterprise workflows where a single missed failure carries real regulatory and financial consequences.
What "Optimized" Actually Means
Optimization is not a single metric. An agent with strong accuracy scores but unsustainable inference costs is not optimized. An agent that responds in milliseconds but hallucinates regulatory thresholds fails its actual job.
TestML evaluates across 20+ dimensions: accuracy, latency, cost per inference, safety scores, compliance coverage, and hallucination rates under adversarial conditions. Every deployment produces a full-spectrum evidence report. No cherry-picking.
For financial workflows, that means testing against MiFID II documentation standards. For medical applications, HIPAA-aligned output validation. For legal teams, citation accuracy against case law. Aditi Verma, Director of AI at a multinational law firm, found that TestML's domain-specific legal evaluation suite cut post-deployment review cycles by half.
Managing Inference Cost Without Sacrificing Quality
Inference costs compound. A multi-agent pipeline processing thousands of calls daily can swing materially based on routing decisions, model selection, and prompt construction. Not every task requires the same model tier.
TestML evaluates cost alongside quality across 340+ enterprise LLM pipelines. Engineering leaders see exactly which tasks warrant higher-capability models and which can route to smaller, faster alternatives without measurable degradation in output quality. Cost optimization is possible. It requires measurement.
Talk to an evaluation engineer to map what this looks like for your pipeline.
Drift Detection Before a Compliance Incident
Silent degradation is the hardest failure class to catch. A provider updates a model and output distributions shift without announcement. A prompt that performed reliably in Q1 behaves differently by Q3 because underlying weights changed.
Ewa Kowalska, TestML's Lead ML Engineer, built automated regression pipelines for exactly this problem. Production monitoring runs evaluation suites on a continuous basis. When drift exceeds a defined threshold in accuracy, latency, or safety metrics, alerts fire before a compliance team or customer notices.
Tom Rigby, VP of AI Engineering at a global insurance carrier, deployed claims-processing agents across seven markets using TestML's monitoring infrastructure. Zero compliance incidents at go-live.
Canary Deployments for LLM Updates
Releasing an updated model version into a production agent pipeline carries real risk. TestML treats LLM changes the same way mature engineering teams treat code releases: staged rollout, measured delta, and clear rollback criteria.
A canary deployment routes a controlled slice of traffic to the updated model while TestML evaluates behavioral change across all 20+ dimensions in real time. If accuracy regresses beyond a defined threshold, or new adversarial vulnerabilities surface, the rollout pauses. David Park, Head of Evaluation Science, built this adversarial testing methodology across three Fortune 500 LLM rollouts before TestML was founded. Every client engagement uses the same approach.
Measuring What Actually Matters
Most teams optimize for metrics they can already measure. TestML makes the hard metrics measurable: regulatory boundary adherence, hallucination rates under adversarial load, cost per quality-weighted output.
Stop guessing about your agent's behavior under production conditions. Book a 45-minute technical review with a TestML evaluation engineer and get a mapped risk surface for your current deployment.