AI Agents

Agents are useful only when they are bounded by real workflows, explicit tools, and operational checks. The writing here treats agents as production systems, not autonomous magic.

Start Here

• Building Reliable AI Agents in Go covers bounded tools, validation, recovery paths, and failure containment.
• Multi-Agent Orchestration: Four Patterns and Their Tradeoffs compares the orchestration shapes that actually survive production.
• AI Agent Architecture Patterns for Production gives the foundational model for tool use and control flow.

What Makes Agents Production-Ready

An agent is production-ready only when the system around it is boring:

• Tool access is scoped and auditable.
• Human approval exists where blast radius is high.
• Outputs are validated before downstream actions.
• Retries and fallbacks are explicit.
• Cost and latency are measured per workflow, not per demo.

Reading Path

For orchestration decisions:

• AI Workflow Automation: Let the Model Decide, Let Code Act
• AI Agent Patterns in Go: Planning, Memory, Recovery

For architecture boundaries:

• AI-Native Architecture Patterns 2026
• Model Context Protocol in Go: Building an MCP Tool Server

For governance and reliability:

• AI Evaluation and Production Governance: A Maturity Model
• AI Security in 2026: Prompt Injection, Agents, and Defenses

Failure Modes

• Giving an agent broad tool access before defining approval boundaries.
• Treating orchestration as a prompt problem instead of a state-management problem.
• Measuring success by task completion while ignoring retries, escalations, and silent failures.
• Letting every team invent its own agent framework, logging format, and evaluation path.

Related Hubs

• AI Operating Systems
• Architecture
• Reliability

References

• OWASP Top 10 for LLM Applications
• NIST AI Risk Management Framework
• The Twelve-Factor App