An AI agent stack consists of LLM models, memory systems, tool integrations, orchestration logic, and observability layers enabling autonomous multi-step reasoning systems.
AI systems are shifting from single-model apps to structured stacks.
Let’s break it down.
- Model Layer
- LLMs
- Multi-model routing
- Specialized inference
- Multi-model orchestration patterns
- Memory Layer
- Vector databases
- Context stores
- Retrieval pipelines
- Memory enables persistent state across sessions
- Tooling Layer
- APIs
- Internal tools
- Action frameworks
- Agents select tools dynamically
- Orchestration
- Planning
- Task splitting
- Multi-agent coordination
- More on architecture
- Observability & Safety
- Logging
- Guardrails
- Human-in-the-loop
- Production AI requires traceability
Example Stack
- LLM (reasoning)
- Fast model (classification)
- Vector DB
- Tool APIs
- Planner agent
- Evaluator agent
- Logging dashboard
Building AI-native systems?
https://brainpath.io
Agent infrastructure → https://brainpath.io/agents
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