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Build AI agents that survive production.

Practical patterns, failure cases, architecture, and governance for agents that call real tools.

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Learn

What an agent is, what it isn’t, and when you should not build one.

What Is an AI Agent (in simple terms)
How an AI agent differs from ChatGPT and automation (Workflow)
How an AI agent makes decisions (and why it is not magic)

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Tool calling, planning vs reactive, and the limits you’ll hit in prod.

What an AI Agent Is Made Of
How an Agent Uses Tools (Basics)
How to Restrict Tool Access

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Patterns that didn’t break production (with code and trade-offs).

React Agent Architecture: Patterns, Pitfalls, Fixes
Task Decomposition Agent Pattern: Break Complex Tasks Down
Routing Agent Pattern: Smart Task-to-Agent Selection

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Build

The stuff between your agent and disaster

Budgets, tracing, policy, rate limits, ops. The unglamorous part.

Containerizing AI Agents (So They Don’t Die at Deploy Time)
Hybrid Architecture (Workflow + Agent)
Multi-Tenant Agent Design (Isolation + Governance)

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Governance & Guardrails

Budgets, approvals, permissions, audit logs — the controls that keep agents safe.

Allowlist vs Blocklist (Why Default-Deny Wins) + Code
Budget Controls for AI Agents (Steps, Time, $) + Code
Cost Limits for Agents (Token + Tool Spend) + Code

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Observability & Monitoring

Logging, traces, metrics, and alerts that keep agent incidents from becoming mysteries.

AI Agent Logging (What to Log, What to Redact, What to Alert On)

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Testing & Evaluation

Unit tests, golden tasks, record/replay, and evals that catch regressions before prod does.

Unit Testing AI Agents (Deterministic, Cheap, and Actually Useful)

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Prompt + runtime tuning without breaking safety: latency, cost, and regression control.

Prompt Optimization for AI Agents (Without Breaking Production)

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How to keep your agent from breaking things

Permissions, budgets, kill switch, idempotency, audit logs.

AI Agent Tool Permissions (With Code)

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Tools & Integrations

APIs, browsers, databases — the stuff your agent will inevitably call.

Browser Tool for AI Agents (With Code)

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Avoid

Failures & Fixes

How agents fail in the real world, and how to stop the bleeding.

Silent Agent Drift (Quality Regression) + Detection + Code
Budget Explosion (When Agents Burn Money) + Fixes + Code
Cascading Tool Failures (How Agents Amplify Outages) + Code

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Things that look smart in demos and break production (with receipts).

No Monitoring (Anti-Pattern) + What to Log + Code
Single-Step Agents (Anti-Pattern) + Fixes + Code
Trusting Tool Output Blindly (Anti-Pattern) + Fixes + Code

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Apply

Build X in 20 minutes (safe by default).

Build Your First AI Agent (Safely, With Code)

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End-to-end agents with code and the parts that went wrong.

AI Support Agent Example (With Code)

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Framework vs framework, agents vs workflows — what breaks in production and what to pick.

AutoGPT vs Production Agents (What You Actually Need) + Code
CrewAI vs LangGraph (Production Comparison) + Code
LangGraph vs AutoGPT (Production Comparison) + Code

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Integrated: production controlOnceOnly

Add guardrails to tool-calling agents

Ship this pattern with governance:

Budgets (steps / spend caps)
Tool permissions (allowlist / blocklist)
Kill switch & incident stop
Idempotency & dedupe
Audit logs & traceability

Try OnceOnly Docs & examples

Integrated mention: OnceOnly is a control layer for production agent systems.

Example policy (concept)

Python

# Example (Python — conceptual)
policy = {
  "budgets": {"steps": 25, "usd": 2.0},
  "tools": {"allow": ["http.get", "browser.search"]},
  "controls": {"kill_switch": True, "idempotency": True, "audit": True},
}

# run = onceonly.run(policy)
# result = run.invoke(agent, input="...")