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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 Are AI Agents
AI Agents vs ChatGPT and Workflows: What's the Difference?
How AI Agents Make Decisions

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

What AI Agents Are Made Of
How AI Agents Use Tools (Basics)
How AI Agents Are Allowed to Use Tools

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

React Agent Pattern: Reliable Task-Handling
Task Decomposition Agent Pattern: Break Complex Tasks
Routing Agent Pattern: Smart Task Routing Between Agents

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Build

Production stack, observability, rate limits, operations.

Agent Runtime: Control the Agent Execution Loop
Tool Execution Layer: Safe Tool Execution for AI Agents
Memory Layer: How Agents Store and Retrieve Memory

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Agent Governance

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
Human-in-the-Loop Approvals (Write Gates) + Code

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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 AI Agents

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

Testing AI Agents: Production Testing Strategy
Eval Harness for AI Agents: Repeatable Evaluations
Golden Datasets: Reliable Test Data for AI Agents

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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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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.

Why AI Agents Fail: Common Production Problems
Agent Drift: When AI Agents Gradually Lose Focus
Infinite Agent Loop: when an AI agent does not stop

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

Anti-Pattern Agent Everywhere: When Everything Becomes an Agent
Anti-Pattern Overengineering Agents: When Architectures Become Too Complex
Anti-Pattern Too Many Tools: When Agents Have Too Many Options

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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.

ReAct Agent in Python: Full Example
Task Decomposition Agent in Python: Full Example
Routing Agent in Python: Full Example

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

AutoGPT vs Production Agents: What's the Difference?
CrewAI vs LangGraph: What's the Difference?
LangChain vs AutoGPT: What's the Difference?

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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="...")