Agent Policy Engine Design: Policy-as-Code for Agent Behavior Enforcement
Teams need programmable policy engines for agent behavior: RBAC, rate limits, and semantic constraints beyond tool-level permissions.
Build Autonomous AI Agents from Scratch
42 bilingual deep-dive articles · All with runnable code · Full Agent engineering lifecycle
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Production Engineering
Sandbox · Permissions · Safety · Isolation · Audit · Evaluation — 6-part series
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Get Started
Build your first Agent in 50 lines of Python
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MCP Protocol
From primer to production: complete tool-calling standard guide
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Multi-Agent Systems
8-agent debate · Market analysis · Orchestration & consensus
📖 Start Here
New to AI Agents? Follow this reading path:
- What Is an AI Agent — The fundamental difference between chatbots and autonomous agents
- Write Your First AI Agent — 50 lines of Python that actually search and compute
- Agent Tool Design Best Practices — 8 rules for tools that models call correctly
- Agent Memory Systems — Short-term, long-term, and RAG explained
- Agent Error Recovery — Four defense lines for self-healing agents
📚 Article Series
🏭 AI Agent Production Engineering Series (6 articles · Complete)
Complete production pipeline from code sandbox to security evaluation: Sandbox → Permissions → Command Safety → Isolation → Audit → Security
Agent Rollback Design: Recovering When Agent Automation Writes Bad Files
When agents write bad files or corrupt state, systematic rollback patterns let you undo the damage.
Agent VERIFIED Deployment Gate Design
How to verify that deployments actually work — structural integrity, page rendering, security headers, and multi-language consistency.
AI Agent Fundamentals
From zero to a complete Agent framework: core concepts, runnable code, tools, memory, and error recovery.
Multi-Agent & Debate Systems
How do multiple Agents collaborate, orchestrate, and debate? From theory to production engineering.
- 📘 Multi-Agent Orchestration
- 📘 Model-Agnostic Agent Design
- 📘 Multi-Agent Debate System Design ← Series Hub
Debate Theory Series L1–L4:
- L1 — Why Debate Beats a Single Answer
- L2 — Structured Debate Protocol
- L3 — Debate Scoring & Consensus
- L4 — Production Deployment
Market Analysis Applied Series:
Agent Multi-Tenancy Design: Tenant Isolation, Resource Quotas, and Cost Partitioning
SaaS platforms running agents for multiple customers need tenant-level isolation, resource quotas, and cost partitioning.
Agent Knowledge Freshness: Staleness Detection, Cache Invalidation, and Fact Verification
Teams need strategies to detect stale knowledge, invalidate caches, and verify facts in long-running agent deployments.
🆕 Latest Posts
Agent Grounding Strategies: Source Citation, Confidence Scoring, and Attribution Chains
Builders need systematic grounding strategies: citing sources, scoring confidence, and maintaining attribution chains for agent outputs.
Agent Compliance Framework: Regulatory Requirements, Evidence Collection, and Compliance Gates
Organizations deploying agents in regulated industries need compliance frameworks with audit-ready evidence collection.
Agent RAG Pipeline Design: Tool-Augmented Retrieval and Multi-Hop Reasoning
Build RAG pipelines where the consumer is an autonomous AI agent that invokes tools to enhance retrieval and enable multi-step reasoning chains
📋 All Posts
Agent Resilience Patterns: Circuit Breakers, Rate Limiting, Bulkheads, and Graceful Degradation
Production agents need more than retries. Covers circuit breakers, token-aware rate limiting, bulkhead isolation, and multi-provider resilience.
Agent Deployment Strategies: Canary, Blue-Green, and Progressive Rollouts for Agent Pipelines
Agent system deployment differs fundamentally from traditional software. Covers canary, blue-green, and progressive rollouts with YAML templates.
Agent Cost Observability: Tracking Tokens, Tool Calls, and Retry Costs
Complete guide to agent cost observability: per-task cost attribution, multi-tenant allocation, budget alerts, and cost-aware model routing.
Agent Release Gate Design: From QA to VERIFIED Production Releases
8-layer release gate system with JSON/YAML gate config schemas.
Agent State Machine Design: Turning Uncontrolled Conversations into Recoverable Workflows
7-state lifecycle with transition table, SQLite persistence, and a recoverable Python skeleton.
Agent Context Window Management: Compressing, Preserving, and Evicting Task State
6 eviction policies, 5 compression strategies, token budget management, cross-window state continuity.
Agent Memory System Design: Short-Term Memory, Long-Term Memory, and Retrieval Boundaries
L0-L3 four-layer architecture + retrieval boundary design + memory lifecycle + hygiene + multi-tenant isolation.
Agent Human Approval Workflow: When Agents Should Pause, Ask, and Continue
Four-tier risk gating + formal approval state machine + timeout escalation chains. Framework-agnostic.
Agent Message Schema Design: Making Multi-Agent Workflows Verifiable and Traceable
Four-layer schema design model + versioning strategy + runnable three-agent reference implementation.
Agent Context Protocol Design: Passing State Across Tools, Memory, and Tasks
Four-layer context protocol architecture with complete Python reference implementation.
Agent Observability: Metrics, Tracing, and Real-Time Alerting for Production AI Agents
OpenTelemetry distributed tracing, Prometheus metrics pipeline, real-time alerting rules.
Agent Security Evaluation: Testing Privilege Escalation, Leakage, and Infinite Loops
From privilege escalation detection to CI/CD security gates with complete Python test harness.
Agent Audit Log Design: Tracing a Complete Tool-Call Chain
8 universal + 5 event-specific fields, trace_id/span_id design, OpenTelemetry integration, log replay.
Agent Runtime Isolation: Docker, Firecracker, VM Sandbox — How to Choose
From Docker containers, Firecracker microVMs, gVisor sandbox to hardware virtualization.
Agent Command Execution Safety: Risk Boundaries for Shell, Filesystem, and Network Access
Command templating, read-only mounts, network allowlists — complete security patterns.
Agent Tool Permission Control: Designing Tool ACLs, Approval Flows, and Least Privilege
RBAC/ABAC/ReBAC model selection, parameter-level access control, and human-in-the-loop approval flows.
Agent Code Sandbox Design: Safe Execution Patterns for AI-Generated Code and Tool Calls
Five-boundary isolation architecture, gVisor vs Firecracker selection, complete Python/Go sandbox code.
AI Agent Evaluation Framework: Measuring What Your Agent Actually Does
5 evaluation dimensions, offline regression testing, LangSmith vs OpenAI Evals comparison.
MCP Protocol Production Guide: Security, Sandbox, and Multi-Server Routing
OAuth authentication, Docker sandboxing, multi-server gateway, OpenTelemetry monitoring.
MCP Protocol Primer: Why AI Agents Need a Unified Tool-Calling Standard
Learn MCP through the LSP analogy, master the Host→Client→Server architecture triangle.
Backtesting & Validation — Accuracy and Judge Weight Calibration Across 100 Historical Debates
A complete backtesting framework delivers hard data on multi-agent debate performance.
Multi-Agent Debate Protocol: 8 Agents in Structured Adversarial Debate with Cross-Examination
3-round structured protocol solving off-topic drift and unscoreable free-form debate.
Multi-Agent Debate × Market Analysis — System Architecture & Data Pipeline
How to feed real market data into an 8-agent structured debate — from data pipeline to specialized Agent roles.
🛠 Tools & Frameworks
These are the core building blocks of Agent engineering, organized by category:
| Category | Tools / Frameworks | Best For |
|---|---|---|
| Agent Frameworks | AutoGen, LangGraph, LangChain, CrewAI, smolagents | Multi-agent collaboration, state flows, tool calling, task orchestration |
| Coding Assistants | Claude Code, Codex, OpenCode | Automated writing, code generation, engineering execution, PR review |
| Protocols & Tool Calling | MCP, Function Calling, JSON Schema | Tool integration, context management, standardized communication |
| Agent Workflows | ReAct, Plan-Execute, LLM-as-Judge | Reasoning loops, task planning, result evaluation, self-correction |
Content on this site covers all these areas — from conceptual understanding to production deployment code.