The Anatomy of an AI Agent: Model, Memory, Tools and Actions
Walks through the four core components of an AI agent architecture and how they fit together into a working system.
The Model Is the Reasoning Engine, Not the Whole System
It's tempting to think of the model as 'the agent,' but the model is really just the component that decides what to do next given the current context. Swap in a different model of similar capability and the agent's behavior should degrade gracefully, not collapse — if it collapses, the architecture around the model is doing too little work.
The model's job narrows to: given everything currently in context, produce either a tool call or a final answer. Everything else — what goes into that context, what happens with the output — is the responsibility of the surrounding system.
Memory Is What Survives Between Steps
Memory in an agent context usually means two different things: the working context for the current task (recent tool results, the conversation so far) and longer-term storage that persists across separate runs (a database of past interactions, user preferences, learned facts). Conflating the two leads to bloated prompts stuffed with irrelevant history.
Good agent design treats short-term context as a scarce, actively managed resource, and long-term memory as a separate retrieval problem — you fetch only the relevant slice of it into context when needed, rather than trying to keep everything in the model's head at once.
Tools and Actions Close the Loop
Tools are the interface between the model's decisions and the real world: APIs, databases, file systems, other services. Actions are the specific calls the model makes through that interface at a given step. The distinction matters for design — you scope tools once, up front, but you audit actions continuously, because that's where things actually go wrong.
A well-architected agent keeps these four pieces cleanly separated: swap the model without rewriting memory handling, add a tool without touching the planning logic, change the memory backend without redesigning the action interface. When they're tangled together, every change becomes a rewrite.
- Model: decides the next action given current context
- Memory: short-term working context plus longer-term persistent storage
- Tools: the interface to the outside world, scoped in advance
- Actions: the specific calls made through that interface at runtime
Key takeaways
- Apply one concrete change from this post before collecting more reading.
- Prefer browser-side tools when the work involves secrets, tokens, or PII.
- Document the why next to the how so the next reviewer inherits context.
FAQ
- Who is this guide on ai for?
- Working developers who need a practical take on the anatomy of an ai agent: model, memory, tools and actions — not a marketing overview. Skim the sections, apply one tip, then come back when you hit an edge case.
- Do I need an account to use the related tools?
- No. code.live tools run in your browser with no signup. Nothing you paste is uploaded to a server for the client-side utilities linked from this post.
- How often is this article updated?
- This post was published September 17, 2026. Fundamentals stay stable; check linked tool pages and official docs when version-specific behavior matters.