AI Agent Architecture: Planning, Memory, and Tools

Every agent architecture, simple or elaborate, runs the same basic loop. The agent observes the current state, decides on an action, executes that action through a tool, and observes the result before deciding again. Everything else in the architecture exists to make that loop accurate, safe, and observable.

Beginning with the loop is useful because it forces the core questions early: what does the agent need to know, what can it do, and what should stop it from acting the moment an input looks wrong. Answering those questions first keeps the rest of the design grounded.

The planning layer is where the agent decides what to do next. It can operate at different levels of sophistication, from a simple next-step choice to a multi-step plan that decomposes a goal and revisits it as tool results come in.

A practical agent uses a bounded planning style: it maintains a small set of high-level goals, picks the immediate next action, and re-plans when results contradict its assumptions. Unbounded planning, where the agent is free to pursue any tactic indefinitely, is rarely appropriate for business systems.

The planning layer is also the place where autonomy boundaries live. Teams define which goals an agent may pursue without approval and at which points control returns to a human.

Agents need two kinds of memory. Short-term memory holds the current conversation or task context, usually limited by the model's context window. Long-term memory persists information across sessions so an agent can remember preferences, prior decisions, and resolved cases.

Architecturally, the two should stay separate. Conversation context is ephemeral and becomes part of the prompt, while durable facts belong in a structured store or vector index that can be retrieved on demand.

Deciding what actually needs to be persisted, and under which access controls, is a design decision with real security and cost implications. Storing everything that passes through an agent is rarely the right answer.

The tool layer is the only part of the architecture that touches external systems, which makes it the natural home for credentials, rate limiting, retries, and idempotency handling. Tools should be thin and contract-driven: a clear schema on the way in, a structured result on the way out.

Keeping tools thin matters because they are the attack surface and the failure surface in one. A tool that silently hides errors, retries destructive operations, or carries too-broad credentials will undermine every layer above it.

Teams should also decide early whether tools are read-only, action-taking, or hybrid, because that classification drives both the permission model and the human approval rules.

The execution loop is where production realities get handled: token budgets, timeouts, maximum iteration counts, retry policies, and interruption handling. An agent stuck in a loop is a real failure mode, and the loop needs explicit termination conditions.

Logging and traceability belong to the loop too. Every step should record which tool was called, with what arguments, what came back, and what the agent decided next. That record is what makes evaluation, debugging, and audit reviews possible.

Finally, the loop is where human approval hooks integrate. High-risk actions pause the loop, surface the decision to a person, and resume only after explicit confirmation, which is the pattern that lets agents take useful action without removing human judgment.