Agentifact Guides
Guides & Analysis
Architecture patterns, protocol comparisons, and production strategies for builders working with autonomous agents. Written with opinions, not press releases.
The Autonomy Audit: Is Anyone Actually Running a Business on AI Agents?
Greg Isenberg says you can hire 5,000 AI employees in a weekend. Liam Ottley made $7M teaching the dream. Air AI just paid $18M to the FTC. We followed the money, read the benchmarks, and talked to the builders. The answer is more nuanced than either side wants to admit.
Reflection and Critique: How AI Agents Can Review and Improve Their Own Output
Reflection loops let agents critique their own outputs before delivery — catching errors and improving quality without extra human review. Here's how to design critic agents, convergence rules, and structured critique schemas.
Human-in-the-Loop: When and How to Insert Human Judgment into AI Agent Pipelines
HITL patterns let you insert human judgment at critical agent decision points without breaking automation flow. Here's how to design triggers, handoffs, and resume patterns for production.
Tool-Calling Loops: The Core Pattern Behind Every Capable AI Agent
The ReAct loop is the fundamental pattern behind every capable AI agent. Here's the canonical implementation with tool contracts, termination logic, context management, and production hardening.
Hierarchical Multi-Agent: How to Build AI Organizations That Self-Coordinate
When a single supervisor can't track 20 workers, you need hierarchy. Learn how to build multi-level agent organizations with delegation protocols, failure escalation, and depth limits.
Event-Driven Agents: Building AI Systems That React in Real Time
Event-driven orchestration decouples agent execution from trigger logic. Agents subscribe to events, react asynchronously, and scale independently. Here's how to build it.
Supervisor-Worker: The Orchestration Pattern That Scales AI Agent Teams
The supervisor-worker pattern lets one planner agent decompose tasks, dispatch specialized workers, and synthesize their results. It's how the best agent systems scale — and the hardest to get right.
Parallel Fan-Out: Running Multiple AI Agents Simultaneously to Cut Latency by 10x
When tasks are independent, running them sequentially is waste. Parallel fan-out dispatches multiple agents simultaneously and merges the results — cutting total time from the sum to the max.
Sequential Chaining: How to Build Multi-Step AI Agent Pipelines That Actually Work
Sequential chaining passes the output of step N as input to step N+1. It's the backbone of every serious agent pipeline — and most builders implement it wrong.
Why No Single Tool Catches More Than 75% of Bugs
Code inspections catch 60%. Unit tests catch 25%. No single technique exceeds 75%. But stack four together and you hit 99%. Here's how — and why AI code makes the math urgent.
Building an Agent Observability Stack That Actually Helps You Debug
When your agent produces wrong output, you need to answer 'why' in under 5 minutes. That requires three layers of observability most builders skip.
HITL vs Full Automation: A Decision Framework for Agent Builders
The question isn't 'can an agent do this?' It's 'what happens when the agent gets it wrong?' That answer determines your architecture.
Six Agent Security Gaps Most Builders Ignore
Prompt injection gets all the attention. The real risks are in tool permissions, state corruption, and credential handling.
The Real Cost of Running Agents in Production
A single agent run can cost $0.003 or $3.00. The difference isn't the model — it's how you architect the system.
Building Your First A2A Pipeline: A Practical Walkthrough
A2A lets agents discover each other, negotiate capabilities, and hand off tasks. Here's how to build a working pipeline from scratch.
Error Recovery Patterns for Production Agents
Five battle-tested patterns for handling LLM failures, tool errors, and state corruption in production agent systems.
LangChain vs CrewAI vs AutoGen: What the Benchmarks Don't Tell You
Benchmarks measure task completion. Production measures error recovery, cost control, and whether you can debug it at 2am.
How to Evaluate an MCP Server Before You Put It in Production
Most MCP servers work in demos. The question is whether they'll survive your production traffic, error handling, and security requirements.