Your Agent Gave the Right Answer for the Wrong Reason — and You Have No Idea

2026-06-04 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

A practical framework for observability and evaluation of agentic AI systems addresses the critical issue of agents producing correct final answers via incorrect intermediate steps, leading to silent failures and performance degradation. The framework defines observability as capturing every agent action—including tool calls, retrievals, and LLM invocations—as structured "spans" using a Python AgentSpan dataclass and AgentTracer context manager. Evaluation then judges these observed steps across three dimensions: faithfulness to context, correctness of steps, and efficiency. This is achieved through fast heuristic evaluators (e.g., step_count_score, tool_error_rate, context_truncation_flag, token_efficiency) and an LLM-as-judge for semantic assessments like faithfulness, using models such as "claude-sonnet-4-20250514". The article details a three-layer evaluation pipeline and identifies five key metrics for agent dashboards, including faithfulness_score and context_truncation_rate.

Key takeaway

For MLOps Engineers deploying agentic AI systems, relying solely on final output evaluation is insufficient and risky. You must instrument every intermediate agent step using a tracing framework to gain observability. Implement a multi-layered evaluation pipeline combining heuristic checks and LLM-as-judge assessments to proactively identify silent failures, ensure faithfulness, and optimize efficiency. Prioritize monitoring faithfulness_score and context_truncation_rate to catch issues before they impact users.

Key insights

Agentic AI systems require deep observability and multi-dimensional evaluation to prevent silent failures and ensure reliable, efficient operation.

Principles

• Separate observability (what happened) from evaluation (was it good).
• Observe agents as a tree of operations, not black boxes.
• Evaluate agent quality across faithfulness, correctness, and efficiency.

Method

Implement a "span" data structure and "TracerContext" to capture every agent step (LLM calls, tool calls, retrievals). Apply heuristic evaluators and an LLM-as-judge in a three-layer pipeline to score faithfulness, step correctness, and efficiency.

In practice

• Wrap key agent operations with AgentSpan and AgentTracer.
• Implement heuristic evaluators for fast, deterministic checks.
• Use LLM-as-judge for semantic evaluations like faithfulness.

Topics

• Agentic AI Systems
• AI Observability
• LLM Evaluation
• Distributed Tracing
• MLOps
• Faithfulness Metrics

Code references

• Arize-ai/phoenix
• explodinggradients/ragas

Best for: AI Engineer, MLOps Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.