AI Agent Production Engineering Series
What this series solves
Taking an AI Agent from demo to production requires solving one core problem: security. This 6-article series progresses through engineering layers — starting with code sandboxing at the lowest level, then building upward through permission control, command safety, runtime isolation, audit logging, and finally automated testing to verify that every protection continues to work. Each article stands alone; read in order to build a complete mental model.
Reading Path
📦 Article 1 · Beginner
Agent Code Sandbox Design: Safe Execution Patterns for AI-Generated Code and Tool Calls
How to protect the host when an AI Agent executes LLM-generated code? A five-layer boundary architecture (kernel, filesystem, network, process, resources) with practical implementation. Covers Docker, gVisor, seccomp trade-offs and adoption roadmap.
⏱ ~25 min · Difficulty: Beginner
🔐 Article 2 · Intermediate
Agent Tool Permission Control: Designing Tool ACLs, Approval Flows, and Least Privilege
Giving an Agent full tool access is a security nightmare. Starting with RBAC and layering ABAC for context-aware fine-grained authorization, paired with approval flows that distribute decisions between humans and machines. Engineering patterns for least privilege.
⏱ ~30 min · Difficulty: Intermediate
🛡️ Article 3 · Upper-Intermediate
Agent Command Execution Safety: Risk Boundaries for Shell, Filesystem, and Network Access
rm -rf, curl | bash, eval injection — real incidents and defense patterns for AI Agent command execution. From dangerous-command denylists and parameterized execution to path sandboxing and network egress control — building a multi-layer command safety line.
⏱ ~30 min · Difficulty: Upper-Intermediate
🏗️ Article 4 · Upper-Intermediate
Agent Runtime Isolation: Docker, Firecracker, VM Sandbox — How to Choose
Docker's shared kernel isn't safe enough? gVisor, Firecracker, Kata — how to choose? From the isolation spectrum to a decision framework, comparing security boundaries, startup latency, and resource overhead across isolation levels for production recommendations.
⏱ ~30 min · Difficulty: Upper-Intermediate
📋 Article 5 · Intermediate
Agent Audit Log Design: Tracing a Complete Tool-Call Chain
Designing Agent audit logs: using trace_id to link LLM decisions, tool calls, approvals, and replay across the full chain. Covers data model, storage strategy, query patterns, and compliance requirements — making every Agent action auditable.
⏱ ~25 min · Difficulty: Intermediate
✅ Article 6 · Advanced
Agent Security Evaluation: Automated Testing for Privilege Escalation, Data Leakage, and Infinite Loops
Building an automated security testing pipeline for AI Agents: pytest-based test framework + privilege escalation, data leakage, and infinite loop detection + CI/CD security gates. Make security testing a mandatory PR checkpoint — manual review doesn't scale.
⏱ ~35 min · Difficulty: Advanced
📖 Start with Article 1
Agent Code Sandbox Design →