agent-ops

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(),
    ]
)