Topic 9 · Agents
Agent architecture & internals
A model in a loop, with tools, state and a reason to stop.
An agent is not a special kind of model. It is ordinary software that calls a model in a loop: build the context, ask the model what to do, run the tool it asks for, add the result to the context, and repeat until the model gives an answer or a limit is hit.
Everything interesting lives in the harness around that loop: which instructions and tools the model sees, how state is stored, when to hand work to a sub-agent, and how to stop. The same model can look brilliant or useless depending on that scaffolding.
In this topic
31
terms, each defined below
The agent loop
An agent booking a meeting. Each lap the runtime assembles context, the model decides, a tool runs, and its result goes back into context. On the last lap the model answers instead of calling a tool and the loop ends. Step through it or let it play.
Transcript
- userFind 30 minutes with Sam next week and send an invite.
Glossary
31 terms
AI agents
Systems where an LLM decides which actions to take, calls tools, looks at the results and repeats until a goal is reached, instead of answering once.
Agentic systems
Software in which LLM decisions steer part of the control flow, from a single tool-calling loop to large multi-agent workflows.
Agent systems engineering
The discipline of making agents dependable in production: runtimes, state, tools, evaluation, security and cost control.
Agent architecture
How an agent is put together: its model, instructions, tools, memory, control loop and any sub-agents or workflow graph around it.
Agent runtime
The software that executes agents: it runs the loop, calls the model, dispatches tools, saves state and enforces limits.
Agent harness
The scaffolding that turns a model into an agent: prompt assembly, tool definitions, output parsing, the loop and its stop rules. The same model can perform very differently in different harnesses.
Agent control loop
The central cycle: assemble context, call the model, run any requested tools and append their results, then repeat until the model answers or a limit is reached.
Reasoning loop
The model's think, act, observe pattern inside the control loop, popularised as ReAct, where intermediate reasoning guides the next action.
Agent orchestration
Coordinating agents, tools and steps: what runs when, in which order or in parallel, and how results flow between them.
Tool-calling loop
The control loop built on function calling: the model emits tool calls, the runtime runs them and returns the results as messages.
Agent state machines
Modelling an agent as explicit states and transitions, such as planning, acting, awaiting approval and done, which makes behaviour predictable and recovery easier.
Agent lifecycle
The stages of one agent instance: created, configured, running, paused or waiting for input, completed or failed, and cleaned up.
Agent sessions
A bounded run or conversation with its own message history, state and resources.
Agent identity
Who the agent is to other systems: its credentials, permissions and audit identity, kept separate from the user it acts for.
Agent configuration
The settings that define an agent: model, sampling settings, instructions, enabled tools, limits and memory options.
Agent instructions
The system prompt and guidelines that set the agent's role, goals, constraints and rules for using tools.
Agent capabilities
What an agent can actually do. It is set by the tools and permissions it is given, not by the model alone.
Agent state
Everything needed to continue an agent from where it is: messages, intermediate results, its plan, counters and pending tool calls.
Tool selection
The model choosing which tool to call, or whether to call one at all, from the tool descriptions and the current context.
Tool invocation
Actually calling the chosen tool with the arguments the model produced.
Tool result handling
Formatting a tool's output, or its error, back into the context, often trimming or summarising large results first.
Tool dispatch
Routing a tool call to its implementation, whether a local function, an MCP server, an HTTP API or a sandboxed process, possibly several in parallel.
Agent termination
Ending the loop: the model gives a final answer, a step, cost or time limit is hit, an error cannot be recovered, or a person stops it.
Subagents
Agents started by another agent for a sub-task. Each gets its own context window and returns only a summary, which keeps the parent's context small.
Agent delegation
Handing a task to another agent, including deciding what context to pass along and what result to expect back.
Multi-agent systems
Systems of several agents with distinct roles that cooperate, such as a planner with workers or a writer with a reviewer.
Multi-agent collaboration
Agents working toward a shared goal by dividing the work, sharing results and checking each other.
Agent coordination
The mechanisms that keep several agents consistent: shared state, locks, turn-taking or a supervising agent.
Agent-to-agent communication
Messages exchanged between agents, directly or through shared memory, increasingly over standard protocols such as A2A.
Task decomposition
Breaking a goal into smaller steps or sub-tasks that can be run, delegated or parallelised.
Autonomous task execution
An agent carrying a task through many steps without human input, inside guardrails and limits.