← Back to all learningsTool selection decisions Multi-step reasoning coherence Memory retrieval efficiency Task completion across production environments Benchmark multiple foundation models Select appropriate models powering the agent Measure model impact on overall quality and latency Intent detection: Does agent understand user intents correctly? Multi-turn conversation: Coherence across conversation turns Memory: Context retrieval accuracy and relevance LLM reasoning/planning: Chain-of-thought alignment Tool-use: Selection accuracy, parameter accuracy, execution alignment Task completion: Did agent meet the goal? Correctness: Factual accuracy Faithfulness: Consistency with conversation history Responsibility/safety: Hallucination, toxicity, bias Cost: Model inference, tool invocation, data processing Tool selection accuracy: Correct tool chosen for task Tool parameter accuracy: Parameters populated correctly Tool call error rate: Frequency of failures Multi-turn function calling accuracy: Correct sequence across turns Grounding accuracy: Is CoT aligned with context and tool data? Faithfulness score: Logical consistency across reasoning Context score: Each step contextually grounded Context retrieval: Accuracy of finding relevant contexts Topic adherence: Stay on predefined domains/topics Topic adherence refusal: Correctly refuse off-topic queries Correctness: Factual accuracy vs ground truth Helpfulness: Effective user assistance Response relevance: Addresses specific query Hallucination: Outputs align with established knowledge Toxicity: No harmful/offensive content Harmfulness: Potentially harmful content detection Cross-organizational standards for tool schema/descriptions API self-onboarding using LLMs to generate schemas Golden datasets from historical API logs Regression testing with synthetic data Tool selection accuracy Tool parameter accuracy Multi-turn function call accuracy LLM simulator with virtual customer personas Ground truth intent pairs from historical interactions Compare agent-generated intents to ground truth Intent correctness Task completion Topic adherence classification/refusal Planning score: Successful subtask assignment Communication score: Interagent message quality Collaboration success rate: Percentage of successful sub-task completion Interagent communication patterns Task handoff accuracy Quality: Correctness, faithfulness, helpfulness Performance: Latency, throughput, resource utilization Responsibility: Safety, toxicity, bias, hallucination Cost: Model inference, tool invocation, human effort High-stakes decisions Edge case evaluation Ground truth labeling LLM-as-a-judge calibration Operational dashboards Alert thresholds Anomaly detection Feedback loops for retraining Customer satisfaction scores First-contact resolution rates Sentiment analysis scores Business outcome metrics Framework-agnostic evaluation preferred - Don't lock into LangChain/LangGraph built-in evals Error recovery is a metric - How well does agent detect, classify, and recover from failures? Tool schema quality matters - Poor descriptions = erroneous selection = wasted tokens/cost LLM simulators for testing - Virtual personas can test at scale before production HITL is indispensable - Automated metrics can't capture all emergent behaviors Tool-use metrics: Directly applicable to delegate system Multi-agent evaluation: Planning, communication, collaboration scores Memory evaluation: Context retrieval accuracy Cost tracking: Model inference + tool invocation [ ] Implement tool selection accuracy tracking [ ] Add task completion metrics to squad-eval [ ] Create golden datasets for regression testing [ ] Consider LLM simulator for squad testing [ ] Add hallucination/toxicity checks to outputs [[AI Agent Benchmarks 2026 Compendium]] [[Test-Time Compute 2026]] [[AI Agent Frameworks 2026]] [[MCP Server Best Practices 2026]]
MCP & Protocols2026-04-17•765 words•4 min read
AI Agent Evaluation Framework 2026
#mcp#rag#llm#langchain
AI Agent Evaluation Framework 2026
Source: AWS Machine Learning Blog (Feb 18, 2026)
Authors: Yunfei Bai, Allie Colin, Kashif Imran, Winnie Xiong (Amazon)
Link: https://aws.amazon.com/blogs/machine-learning/evaluating-ai-agents-real-world-lessons-from-building-agentic-systems-at-amazon/
Why Agent Evaluation is Different
Traditional LLM evaluation treats systems as black boxes, evaluating only final outputs. Agents require evaluating:
Key insight: "The new paradigm assesses not only the underlying model performance but also the emergent behaviors of the complete system."
Three-Layer Evaluation Architecture
Bottom Layer: Foundation Model Benchmarks
Middle Layer: Component Evaluation
Upper Layer: Final Output Quality
Core Metrics (Amazon Bedrock AgentCore Evaluations)
Tool-Use Metrics
Reasoning Metrics
Memory Metrics
Quality Metrics
Safety Metrics
Real-World Amazon Examples
1. Shopping Assistant (1000s of Tools)
Challenge: Onboard hundreds/thousands of APIs as agent tools
Solution:
Metrics:
2. Customer Service Agent (Intent Detection)
Challenge: Accurate intent detection for routing
Solution:
Metrics:
3. Multi-Agent Systems (Seller Assistant)
Architecture: Orchestrator + specialized subagents
Metrics:
Best Practices
1. Holistic Evaluation
Four dimensions: quality, performance, responsibility, cost
2. Human-in-the-Loop (HITL)
Critical for:
Target: 0.80+ Spearman correlation with human evaluators