Agent engineering is the discipline of designing AI agents with clear workflows, memory, tooling, and safety. Agent engineering combines software design, prompt engineering, and system-level controls. Effective agent engineering defines how agents reason, access tools, and interact with users.
When doing agent engineering, build clear interfaces for tools, define memory scopes, and set boundaries for autonomous actions. Test agents with real-world scenarios and measure their behavior with LLM evaluation. Agent engineering must include fallbacks and human-in-the-loop control to manage edge cases. Use agent engineering patterns: modular skills, guarded tool calls, and retry policies.
Instrument agents for observability, and use agent optimization practices to tune performance. Agent engineering produces agents that deliver consistent business value while reducing risk.
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