Agentic AI Notebook
0What is an AI Agent?1Why LLMs Need Agents2Anatomy of an Agent3Agent Lifecycle4Core Concepts5Agent Capabilities6Types of Agents7Agent Architectures8Agent Terminology9Current Agent Landscape10Planning11Reflection12Multi Tool13Self Correction14Build First AI Agent
Agent Foundations
Phase 4Module 4 of 15

Agent Lifecycle

Agents degrade from model updates and prompt drift — lifecycle discipline keeps them reliable over months.

Like a satellite: design, build, test, launch, monitor telemetry, patch from orbit.

Visual Workflows

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Lifecycle Flow

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Linear path with feedback loop from production failures back to develop.

Key Takeaways

  • 1.Design: goals, tools, metrics, success criteria.
  • 2.Develop: prompts, integrations, tracing.
  • 3.Evaluate: golden sets, red-teaming, regression.
  • 4.Deploy: monitoring, canaries, scaling.
  • 5.Iterate: feedback, drift detection, improvements.

Real Example

Scenario

Support agent v2: production traces show 15% SQL tool failures (wrong column names). Team adds schema validation in Develop, 20 golden tickets in Evaluate, canary at 5% traffic, then full Deploy after failure rate drops below 2%.

What you would do

Iterate from prod failures: add `validate_sql_columns` in Develop, extend the golden set with the 15 failing queries in Evaluate, canary at 5% in Deploy, and promote only when `sql_tool_error_rate < 0.02` for 48 hours. Monitor: regression pass rate on every prompt change.

Practice Task

Sketch a one-page lifecycle for an agent you might build: Design success metrics, Develop tracing setup, Evaluate golden tasks (list 3), Deploy canary percentage, Iterate feedback source.

Code Walkthrough

Highlighted lines show where Agent Lifecycle happens in the code.

Agent Lifecycle
1golden_cases = [2    {"input": "Refund status for order 8821", "expect_tool": "lookup_order"},3    {"input": "Cancel subscription for user@co.com", "expect_tool": "cancel_sub"},4]5
6def run_eval(agent_fn):  # define a reusable function7    passed = 08    for case in golden_cases:9        trace = agent_fn(case["input"])10        if trace.get("tool") == case["expect_tool"]:11            passed += 112    return passed / len(golden_cases)  # return the result13
14score = run_eval(my_support_agent)  # key line for Agent Lifecycle15print(f"Eval score: {score:.0%} — deploy only if >= 0.9 and canary stable"  # show output for debugging

Cheat Sheet

Quick recap

quick ref
  • •Design metrics before building
  • •Trace from day one in develop
  • •Eval suite before production
  • •Canary deploy then full rollout
  • •Iterate from failure logs

Common Mistakes

  • ✕Skipping evaluation for Agent Lifecycle before production
  • ✕No logging or tracing around agent lifecycle steps
  • ✕Ignoring cost and latency implications