An AI agent is a system, not a model. The model decides the next step, the harness runs the loop and enforces the rules around it, an execution environment supplies files or compute when a task needs them, and an application connects the whole arrangement to the person using it. The intent behind a request reaches this system only through the input and instructions it receives, and nothing in that chain guarantees the agent will act on that intent correctly. Most of the architecture work is therefore about boundaries, checks and visibility.
The four layers of an agent system
Anthropic defines an agent as an AI model that directs its own processes and tool use to accomplish a task, rather than following only a fixed script. OpenAI’s architecture documentation divides its product into a harness, an execution environment and an application server. Read together, the two views give four working layers:
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| Layer | What it does | How vendor documentation describes it |
|---|---|---|
| Model | Produces decisions: either a user-facing answer or a structured request to use a tool | Anthropic: directs its own processes and tool use |
| Harness | Runs the model-and-tool loop, maintains the session, supplies instructions and tool definitions, mediates tool calls, manages context and handles errors | OpenAI: runs the model and tool loop and maintains the session |
| Execution environment | Hosts the commands, code and files a task may need | OpenAI: optional; may be absent, hosted or self-hosted |
| Application | Submits work to the agent, receives events, handles function tools and connects to the product the user sees | OpenAI: the application server submits work and receives events |
Treat this as a design model rather than a product specification. Vendors draw the boundaries differently. Google Cloud notes that the line between a harness and an orchestration framework varies by product and implementation.
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The harness is where most architecture decisions live. A model on its own can only produce text describing an action. The harness is what carries the action out, checks it first, or stops it. Google Cloud’s harness explainer lists retrieval, execution, handling of returned results, task state, permissions, errors, visibility and evaluation as the responsibilities of the surrounding software. OpenAI adds context-window management, which matters more as a task grows.
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In practical terms, a harness typically handles:
- Instructions. The system prompt that sets the agent’s role and intended behavior.
- Tool definitions. The functions or APIs the model may request, described so the model can call them.
- Permission checks. Authorization between the model’s request and the external system it targets.
- Context and state. Appending tool results to the working context and deciding what to keep, summarize or drop.
- Errors and timeouts. Deciding what happens when a tool fails, hangs or returns something unusable.
- Visibility. Logs or traces of what was requested and what happened, along with cost tracking and evaluation.
The OpenAI Agents SDK describes an agent configuration in the same terms: instructions, a model and tools. Instructions set the system prompt and intended behavior, while tools give the model callable functions or APIs.
How the loop runs
An agent repeats a cycle in which it plans, acts through tools, observes the results, and then continues or stops. The model never executes a tool itself. It requests one, and the harness decides whether that request runs. A single model response with no tool execution is a simpler interaction, not an agent loop. The steps below show the cycle the harness manages.
- Receive the user’s goal and the constraints that apply to it.
- Assemble the instructions and the task context the model needs.
- Ask the model for a response. It may be a user-facing answer or a structured request to use a tool.
- Have the harness authorize and execute the requested tool call, then append the result to the context.
- Ask the model to interpret that result and then continue, finish, or ask a person for input.
- Stop at a defined completion condition, and keep or summarize the state that future work will need.
OpenAI’s description of its Codex loop shows the mechanics in detail. Tool output is appended to the original prompt and used in another inference call. The cycle ends when the model stops requesting tools and produces an assistant message. Conversation history grows with every pass, so managing the context window is a harness responsibility.
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The following scenario is an illustration, not a vendor example. A user asks an agent to tidy an exports folder. In the first pass, the model requests a file listing. The harness checks read permission for that folder, runs the listing and appends the result. In the second pass, the model proposes deleting files it judges to be old duplicates. The harness cannot know whether that judgment matches what the user meant. A well-designed system pauses for confirmation at this point, because deletion is hard to reverse. If the original instruction had said to move, not delete, any file older than 90 days, the goal and its limits would be explicit and the harness could check each proposed action against them.
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Intent: what the system can and cannot know
In agent architecture, intent means the user’s desired outcome plus the constraints placed on the work, expressed through input and instructions. It is a useful design concept. It does not mean the agent has reliable access to what a person meant but did not say, and an agent’s behavior is never guaranteed to match intent. Anthropic warns that agents with less human oversight can misread user intent and take unintended actions.
The practical response is to state intent clearly enough that the harness can check actions against it. Useful constraints include:
- Scope. Which systems, folders, accounts or records the agent may touch.
- Restricted actions. Operations that are forbidden or need confirmation, such as deleting, sending or spending.
- Success. What a finished result looks like, so the agent knows when to stop.
- Clarification triggers. Cases in which the agent should ask rather than act, particularly when a goal could cause meaningful side effects and its wording admits more than one reading.
Choosing how the agent is run
The first architecture decision is who owns the loop and its state. OpenAI’s comparison separates three options by intended use, runtime location, integration effort, state handling, tool execution and environment. These are vendor-specific examples of general decision axes, not universal categories. Product APIs and SDKs change often, so confirm current behavior in each vendor’s documentation before implementing. The descriptions here reflect official OpenAI, Anthropic and Google Cloud documentation as of October 2026.
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| Option | Best fit | What to compare before choosing |
|---|---|---|
| Managed agent runtime (OpenAI’s example: the managed Agents API) | Teams that want the provider to manage more of the session and infrastructure | Integration effort, state retention, available tools, environment control, portability, and which deployment tasks remain yours |
| SDK inside your application (OpenAI’s example: the Agents SDK) | Teams that need control over deployment, storage, approvals and integration | Developer effort, who owns state, runtime support, and which orchestration patterns are available |
| Direct model API (OpenAI’s example: the Responses API) | Teams building a custom loop, or making a model call for a bounded interaction | Control, implementation effort, manual history and state management, and where tool execution happens |
Across these options the pattern is consistent. Managed APIs reduce integration work. SDKs give the application more control over deployment and storage. Direct integration leaves more of the loop and the state handling to the developer.
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Execution environments: none, hosted or self-hosted
Not every agent needs an execution environment. An environment is where commands, code and files may run, and it is separate from the harness. Decide first whether the task needs files or compute at all.
| Setup | Use when | What you need to plan |
|---|---|---|
| No environment | The agent answers questions or calls remote tools and needs no local files or compute | Tool connectivity and permissions. There is no shell, workspace or code executor. |
| Hosted environment | The work needs scripts, files, code or custom software, and you want the provider to supply the environment | Provisioning, network access, lifecycle, persistence and operational ownership, checked against the provider’s current terms |
| Self-hosted environment | The work must run inside private networks or on custom infrastructure | You own provisioning, reconnection, shutdown and preservation of files |
Single agent or multiple agents
Start with one agent. Google Cloud advises beginning development with a single agent so you can refine the core logic, prompt and tool definitions before dividing the work. Add specialists when distinct responsibilities justify the routing, context management, evaluation, permissions and coordination they bring.
When you do split the work, the OpenAI Agents SDK documents two composition patterns:
| Pattern | How it works | Main advantage |
|---|---|---|
| Single agent | One prompt and one set of tools covers the job | Simplicity; no handoff points to manage. Tool selection and context complexity grow with scope. |
| Manager | A manager agent keeps control and calls specialists as tools | One central place to apply controls such as guardrails or rate limits |
| Handoff | A specialist takes over the conversation | The specialist can focus on its task without a central manager retaining control |
Multi-agent designs can decompose complex objectives, but Google Cloud calls out the added evaluation, security, reliability, communication and computational cost they require. Nothing in that guidance shows that adding agents automatically makes a system more reliable or better performing.
Safety and reliability controls
Autonomy creates four recurring risks: mistaken interpretation of the goal, prompt injection, excessive tool permissions, and exposure of the execution environment. Anthropic notes that a well-trained model can still be exploited through a poorly configured harness, an overly permissive tool or an exposed environment. The boundaries therefore belong in the surrounding system, not only in the model’s training.
The harness is where most of these controls are enforced:
- Least-privilege access to tools and data, so each tool can reach only what its job requires.
- Error handling and timeouts, with defined behavior when a call fails or hangs.
- Retained state sufficient to continue coherently.
- Logs or traces that show which actions were requested, authorized and executed.
- A way to stop the agent or escalate to a person.
- User confirmation before high-impact or difficult-to-reverse actions.
These are design implications of the documented risks, not a claim that any vendor’s default settings guarantee safety. Verify each control against the product you use.
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When an agent misbehaves, the layers above suggest where to look first. The symptoms below are illustrative patterns that follow from the architecture described in this article, not measured failure rates.
Quick Recap
| Symptom | Layer to check first | Question to answer |
|---|---|---|
| Acts on a vague request in a way the user did not expect | Intent and instructions | Were scope, restricted actions and the stop condition stated, and was confirmation required? |
| Can read or change more than the task needs | Tool definitions and permissions | Does each tool reach only the data its job requires? |
| Loses track of earlier constraints in a long task | Harness context management | Is history growing unchecked, and what is being summarized or dropped? |
| Cannot run a script or read a workspace file | Execution environment | Was the setup chosen without an environment even though the task needed files or compute? |
| Loses work after a restart or reconnect | State and, if self-hosted, file preservation | Who owns persistence, reconnection and shutdown? |
| No record of what the agent did | Harness visibility | Are logs or traces capturing requests, authorization and results? |
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