Amazon Bedrock AgentCore Adds Custom Lambda-Based Evaluators for AI Agents
Summary
- • Amazon Bedrock AgentCore Evaluations now supports custom code-based evaluators via AWS Lambda
- • Deterministic checks replace LLM-as-a-Judge for schema validation, accuracy, and PII detection
- • Evaluators run in on-demand CI/CD gate mode or live production traffic scoring mode
- • No foundation model tokens consumed, making evaluation cheaper and fully reproducible
Details
Bedrock AgentCore adds custom Lambda-based evaluators
Amazon Bedrock AgentCore Evaluations now supports custom code-based evaluators via AWS Lambda, extending its LLM-as-a-Judge system with deterministic, developer-controlled scoring logic for production agent quality control.
On-demand and online evaluation modes
Evaluators run in on-demand mode (acting as CI/CD gates during development) and online mode (scoring live production traffic). The same Lambda function is reusable across multiple agent frameworks without consuming FM tokens per request.
Four evaluator types demonstrated for a financial agent
Demonstrated use cases include: tool response schema validation (catches malformed JSON at tool boundaries), numerical accuracy checks against reference sources, workflow contract compliance (verifying tool call sequences like broker-identification before accessing financial profiles), and PII detection.
Code-based checks outperform LLMs for deterministic rules
For schema conformance, numerical tolerances, and compliance workflows, code-based evaluators produce identical results on identical inputs at lower cost. LLMs are non-deterministic and consume FM tokens per request, making them poorly suited for objective rule-based checks.
Feature targets regulated sectors and production-grade deployments
Financial services and other regulated domains require auditability and deterministic rule enforcement that probabilistic LLM evaluators cannot reliably provide. Custom code evaluators close the gap between prototype and production-grade agent deployments.
Composable with LLM judges; framework-agnostic
Custom Lambda evaluators can be combined with built-in LLM-as-a-Judge evaluators for hybrid pipelines, and work across different agent frameworks for consistent quality assessment regardless of how agents were built.
Product Launch = new feature release; New Tech = new capability; Tech Info = implementation detail; Insight = analysis or implication; Market Impact = business/sector relevance
What This Means
For AI practitioners building production agents — especially in regulated industries — this closes a meaningful gap: LLM-based evaluators are expensive, non-deterministic, and unreliable for rule-based checks like schema validation or numerical accuracy. Custom Lambda evaluators bring software-engineering-grade quality gates (reproducible, auditable, cheap) into the same evaluation pipeline as LLM judges, making it far more practical to enforce compliance and correctness at scale. Teams can now treat agent evaluation with the same rigor as traditional software testing, embedding deterministic checks directly into CI/CD and live traffic monitoring.