Evaluation & AI Reliability · core
Agent Observability
Runs, steps, prompts, model calls, tool calls, tokens, costs, errors, state changes, and outcome metrics.
Mental model
Agent observability connects the final outcome to every decision and side effect. A run needs stable IDs, step spans, inputs, outputs, costs, errors, and redaction.
How to study Agent Observability
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use A Survey on AgentOps: Categorization, Challenges, and Future Directions, Observability in LLM Applications (Hamel Husain), OpenTelemetry GenAI Semantic Conventions to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Agent Observability with Quality, Cost & Latency Measurement, Tracing & Replay. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Design exercise: Agent Observability and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Learn from primary sources
Practice and explain it back
Design exercise: Agent Observability
Runs, steps, prompts, model calls, tool calls, tokens, costs, errors, state changes, and outcome metrics. Implement designOutline() returning non-empty values for: runIdentity, stepSpans, redaction. Each value must name a concrete mechanism or decision.
Expected evidence: A design outline with runIdentity, stepSpans, redaction plus an explicit failure mode or trade-off.
Open the interactive drill →Review prompts
- What identifier makes agent traces useful, and what breaks without it?
Build evidence
Synthesize: Evaluation & AI Reliability
Build an evidence-backed evaluation and observability system for models, tools, and agents. Produce one working system, benchmark, or evidence-backed design that integrates the path.
- Implements or precisely models the core mechanisms from all three milestones
- Includes at least one injected failure or adversarial case and demonstrates recovery
- Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
- Ships a concise architecture note explaining decisions, trade-offs, and remaining risks