AgentCore agents with tools
Agents running on Amazon Bedrock AgentCore that call APIs, Lambda functions and enterprise tools to look up and change records. Existing Bedrock Agents Classic deployments with action groups are tested the same way.
SERVICES / CLOUD PLATFORMS
Evaluate agents built on Amazon Bedrock.
Independent testing and evaluation for AI agents built on AWS, from Amazon Bedrock AgentCore and knowledge bases to multi-agent workflows, measured against your business outcomes.
Common AWS agent patterns
Knowledge-backed assistants
Agents grounded in Bedrock Knowledge Bases. Retrieval quality, citation accuracy and omissions directly shape answer quality.
Multi-agent and custom runtimes
Supervisor and specialist agents, or open-source agent frameworks running on Bedrock AgentCore. Routing and handoffs need their own tests.
Quality issues we look for
Wrong or malformed actions
Incorrect function selection, missing parameters, or arguments that pass schema validation but violate business rules.
Retrieval that misleads
Relevant documents not retrieved, outdated content preferred, or answers that blend retrieved facts with unsupported claims.
Silent partial completion
The agent reports success after only some steps of a multi-step workflow have actually completed.
What we assess
End-to-end task completion
Whether the agent reaches the intended outcome across your selected workflows, tools and handoffs, verified against resulting state.
Retrieval and grounding
Whether retrieved context is relevant and sufficient, and whether answers stay faithful to it, scored with calibrated rubrics.
Model and configuration comparisons
Bedrock gives access to several model families. We compare candidate models, prompts and settings on the same reviewed dataset, with latency and cost.
Working with native AWS evaluation
AgentCore and Bedrock evaluations
Where they fit, we use Amazon Bedrock AgentCore Evaluations for agents and Bedrock's model and knowledge-base evaluations, including LLM-as-a-judge, supplied with your own datasets and rubrics.
Calibrated against your experts
Native scores are compared with domain reviewer labels before they are used for decisions. Disagreements are documented, not averaged away.
Recorded configuration
Model IDs, agent versions, prompts and knowledge-base snapshots are recorded with every run so comparisons stay fair.
Plan the engagement
Your inputs
A test alias or environment, IAM access agreed with your team, version details, business rules, example tasks and a domain reviewer.
Your deliverables
An agreed benchmark, prioritized findings, reusable evaluation assets and recommendations for the next iteration.
Support after handover
We can retest fixes, extend coverage to new AWS workflows or move to recurring Managed Evaluations as your agent changes. Platform usage and licenses stay in your account.
Make your next decision with evidence.
Start with one workflow, clear success criteria, and an evaluation plan your team can use.