AI Agent Architecture

An AI agent is not a more capable chatbot. It is a system architecture: a loop in which a language model perceives state, reasons over a task, selects and invokes tools, evaluates results, and decides whether to continue or return a result. The distinction between an LLM call and an agent system is an architectural distinction—not a capability distinction—and it has direct implications for how agent systems fail, how they are evaluated, and how much engineering they require to be production-ready.

This distinction matters operationally. A standard LLM call is stateless and single-turn: you send a prompt, you receive a completion. The intelligence is in the model; the responsibility for planning and sequencing lives in the application code that calls it. An agent architecture internalises that loop. The model decides what to do next—which tool to invoke, whether the result satisfies the task, whether to attempt a retry or escalate. The model has agency over execution sequencing, which is both the source of its power and the source of its novel failure modes.

What distinguishes an agent from a standard LLM call

The structural difference between an LLM call and an agent is the presence of a decision loop controlled by the model. In a standard pipeline, the application logic drives execution: call model, get completion, parse output, proceed. The model is one step in a deterministic sequence.

In an agent architecture, the model itself drives the loop. Given a task description and a set of available tools, the model determines the next action. It calls a tool, receives a result, evaluates whether that result satisfies the task, and either returns a response or selects the next action. This loop continues until the task is complete, an exit condition is reached, or a hard turn limit is triggered.

The practical consequences of this difference:

Core components of an agent architecture

A production-ready agent system has four functional layers, each with its own engineering concerns:

The reasoning model. The language model that evaluates state, generates plans, and selects next actions. Model selection involves tradeoffs between reasoning capability, latency, cost, and output predictability. More capable models reduce hallucination in tool selection and argument generation but increase cost-per-step in multi-turn workflows. The model is not the only variable in system quality — tool design and evaluation infrastructure matter more consistently across different task types.

The tool layer. The set of functions, APIs, databases, and external systems the agent can invoke. Tool design is where most agent failures originate: ambiguous tool descriptions lead to incorrect tool selection; insufficient error handling produces untyped failure states that the model cannot reason over; tools that return unstructured text rather than typed outputs produce unreliable downstream behaviour. Good tool design is a system design discipline, not a prompt engineering problem.

The state and memory layer. Agents that operate across multiple steps require a mechanism to track prior actions, results, and context. Memory implementations range from in-context window management (fast, bounded by context length) to external vector stores (scalable, with retrieval latency) to structured session state (reliable, requires schema design). The choice depends on session length, context window constraints, and latency requirements.

The execution controller. The orchestration layer that manages turn limits, error thresholds, retry logic, and hard boundaries on what actions an agent can take. This layer is the primary reliability control mechanism. It determines what the system does when the model loops, when a tool returns an error, when a downstream service is unavailable, and when a privileged action is requested.

Orchestration patterns and tool use

Several orchestration patterns have stabilised in production agent systems. The right choice depends on task structure, latency tolerance, and the relative cost of mid-execution errors versus planning errors.

ReAct (Reasoning + Acting). The model alternates between reasoning steps — planning what to do next, evaluating the previous result — and action steps — invoking a tool, waiting for a result. Observable, debuggable, and the appropriate starting point for the majority of agent implementations. The trace of reasoning steps is one of the most useful diagnostics for agent failure analysis.

Plan-and-execute. The model generates a complete execution plan before beginning any tool invocations. The plan is then executed, potentially by a separate execution model with lower capability requirements. Reduces mid-task context drift and improves predictability, but shifts failure risk to the upfront planning step. Appropriate for tasks with well-defined structure and low ambiguity.

Multi-agent orchestration. Specialist sub-agents handle defined task scopes and return results to a coordinating orchestrator agent. Increases system complexity and debugging overhead significantly. Appropriate only when task decomposition produces clear isolation of genuinely distinct specialisations — not as a default architecture for adding capability.

Tool design guidelines that determine production success consistently across all orchestration patterns:

Reliability, evaluation, and failure modes

Agent systems fail in categories that do not exist in simple LLM pipelines. Each failure category requires a different mitigation strategy:

Looping. The agent continues iterating because no exit condition is ever satisfied. The model generates a plan, executes a tool call, evaluates the result as insufficient, and generates a new plan — indefinitely. Hard turn limits that surface a structured failure rather than a silent hang are non-negotiable in production.

Hallucinated tool use. The model generates tool call arguments that do not match valid options — citing parameters that don't exist, fabricating IDs, constructing malformed API payloads. All tool outputs must be validated before acting on them, and all tool call arguments should be schema-validated before dispatch.

Compounding errors. An error or suboptimal result in step 2 propagates through steps 3–6 before the failure becomes visible in the final output. The agent's plan may be internally coherent while being founded on incorrect intermediate results. Step-level evaluation — was each action correct, not just the final answer — is required to detect this class of failure.

Unsafe action execution. Agents with access to write, send, or delete operations can cause irreversible harm through both correct and incorrect execution paths. Privileged tools require human-in-the-loop checkpoints; the implementation pattern (requiring explicit user confirmation before side-effecting tool dispatch) is standard practice and not optional.

Evaluation for agent systems requires test suites that exercise multi-step task paths, not just single-turn quality. Trajectory evaluation — was each step taken correctly, did the model select the right tool, were the arguments valid — provides higher signal than final output evaluation alone. Final output quality can be acceptable even when the execution path had intermediate failures that happen to cancel out; trajectory evaluation surfaces the actual reliability picture.

When agent architecture is the right choice

Agent architecture adds significant engineering complexity to any AI system. It is the right choice in specific conditions, and the wrong choice in many situations where it is commonly proposed.

Agent architecture is appropriate when:

Agent architecture is the wrong choice when:

Logic Grid Studio's AI Systems service covers the architecture, implementation, and evaluation of agent systems as part of a broader AI integration engagement. The Services page describes how this relates to LLM integration, platform engineering, and software delivery in practice.