The AI Agent Stack: Models, Tools, Memory, Evals, and Deployment
A pillar guide to the AI agent stack, covering models, tools, memory, orchestration, evaluation, observability, security, and deployment.
The AI Agent Stack: Models, Tools, Memory, Evals, and Deployment
The AI agent stack is the set of components needed to build agents that can do useful work reliably. A model is only one part of the system.
Core layers
- Models for reasoning, language, and planning.
- Tools and APIs for taking action.
- Memory and retrieval for context.
- Orchestration for multi-step workflows.
- Evals for testing quality and regressions.
- Observability for logs, traces, and monitoring.
- Security for permissions and approvals.
- Deployment for scaling and operating agents.
Why the stack matters
Agent products fail when they rely only on model quality. Real workflows need context, boundaries, feedback, and inspection.
The best agent stack makes autonomy useful while keeping humans able to understand, evaluate, and control what the system does.
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