agent-ops

name	agent-ops
description	This skill should be used when the user asks about agent architecture, evaluation, metrics, production monitoring, debugging agents, or best practices for building reliable AI agents. Use for questions like "evaluate my agent", "set up production monitoring", "add guardrails", "detect hallucinations", "agent anti-patterns", "compare experiments", "create evaluation dataset".

Agent Operations: Build, Evaluate, and Monitor AI Agents

This skill covers the agent lifecycle beyond basic tracing: architecture patterns, evaluation, metrics, and production monitoring. All examples use Opik for observability — for SDK details (tracing, integrations, span types), load the opik skill.

The Agent Lifecycle

1. Instrument — Add Opik tracing to make your agent's behavior visible (see opik skill)
2. Evaluate — Measure performance with datasets, metrics, and experiments
3. Monitor — Track quality, cost, and reliability in production
4. Optimize — Improve based on data from evaluation and production traces

Agent Architecture Patterns

Trace every component of your agent with appropriate span types:

import opik

@opik.track(name="research_agent")
def agent(query: str) -> str:
    plan = plan_action(query)        # general span
    results = execute_tool(plan)     # tool span
    return generate_response(results) # llm span

@opik.track(type="tool")
def execute_tool(action: dict) -> str:
    return search_web(action["query"])

@opik.track(type="llm")
def generate_response(context: str) -> str:
    return llm_call(context)

What to Trace

Component	Span Type	Key Data
Planning	general	Reasoning steps, decisions
Tool calls	tool	Tool name, parameters, results
LLM calls	llm	Prompt, response, tokens
Retrieval	tool	Query, documents
Validation	guardrail	Check results, pass/fail

Evaluation

Evaluate agents at multiple levels — end-to-end and per-component:

from opik.evaluation import evaluate
from opik.evaluation.metrics import AnswerRelevance, Hallucination, AgentTaskCompletion

results = evaluate(
    experiment_name="agent-v2",
    dataset=dataset,
    task=lambda item: {"output": agent(item["input"])},
    scoring_metrics=[
        AnswerRelevance(),
        Hallucination(),
        AgentTaskCompletion(),
    ]
)

Built-in Agent Metrics

Metric	What It Measures
AgentTaskCompletion	Did the agent fulfill its task?
AgentToolCorrectness	Were tools used correctly?
TrajectoryAccuracy	Did actions match expected sequence?
AnswerRelevance	Does the answer address the question?
Hallucination	Are there unsupported claims?

41 Total Built-in Metrics

Heuristic (Equals, Contains, BLEU, ROUGE, BERTScore, IsJson, etc.), LLM-as-Judge (AnswerRelevance, Hallucination, Usefulness, GEval, etc.), RAG (ContextPrecision, ContextRecall, Faithfulness), and conversation metrics. See references/evaluation.md for the full list.

Production Monitoring

• Dashboards — Visualize quality, cost, latency, and error trends
• Online evaluation — Automatically score production traces with LLM-as-Judge
• Alerts — Get notified when metrics deviate (quality drops, cost spikes, error rates)
• Guardrails — PII detection, topic validation, custom safety checks
• Opik Assist — AI-powered root cause analysis for failed traces

Common Anti-Patterns

Category	Anti-Pattern
Reliability	Unbounded loops, retry storms, silent failures
Security	Prompt injection, privilege escalation, data leakage
Observability	Late tracing (missing input), orphaned spans
Tools	Tool loops, hallucinated tools, parameter errors

Detailed References

Topic	Reference File
Agent architecture, reliability, security patterns	references/agent-patterns.md
Evaluation datasets, experiments, all 41 metrics	references/evaluation.md
Production dashboards, alerts, guardrails, cost tracking	references/production.md