The Tool Desk
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What makes a system an AI agent?
An AI agent pursues a goal through some degree of autonomous decision-making, often by using tools and taking multiple steps. That flexibility can help with open-ended work involving decisions, external information, or coordination across systems. It also makes the system harder to control and can add model calls, latency, and cost.
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Does this task need an agent?
Ask whether software can define the steps in advance or whether the system must choose and execute them as the work unfolds. Google Cloud recommends considering simpler generative-AI approaches when work is predictable, highly structured, or can be handled in one call. See Google Cloud’s agentic AI design-pattern guidance.
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- Consider an agent when the task needs open-ended planning, decisions across multiple steps, or actions through tools.
Before choosing a design, state the desired outcome, how success will be judged, what information the system may access, what it may change, and which actions require approval. Also account for task complexity, latency and performance expectations, cost, and the need for human involvement. Revisit these decisions if the workload changes.
Which architecture should you start with?
Begin with the simplest design that can meet the task’s requirements. Google Cloud describes single-agent, multi-agent, and iterative-refinement patterns; none is automatically best for every workload. Compare them by task structure, responsibilities, control, quality, latency, cost, and risk.
| Pattern | Use it when | Trade-off to manage |
|---|---|---|
| Fixed workflow or single model call | The work is predictable and its steps can be specified in advance. | Less adaptable when the system must make open-ended decisions. |
| Single agent with tools | One coherent job requires the system to choose among tools or steps. | Tool ambiguity and unclear boundaries can lead to poor choices. |
| Manager with specialist agents | A central agent should retain workflow control, call specialists, and synthesize results. | Coordination adds complexity and model calls. |
| Peer handoffs | Distinct agents can take over separate parts of a task without one central manager controlling every step. | Handoffs add coordination and can make control harder to follow. |
| Iterative refinement | A draft can benefit from critique and revision against a defined quality bar. | Each loop adds latency and cost; set a stopping threshold and maximum iterations. |
Start with one agent
A single agent with a defined tool set and a comprehensive system prompt is a useful baseline. Refine its core logic, prompt, and tool definitions before adding specialists or orchestration. Split work across agents only when the task contains distinct subtasks that justify the extra coordination.
Choose orchestration based on control
In a manager pattern, one agent controls the workflow and invokes specialists as tools; this suits work where a central agent must remain responsible for the user-facing result. In a decentralized pattern, agents hand execution to peers; this can suit work where specialists can take over. The choice should reflect how the work is divided and who must preserve control, not an assumption that more agents produce better results.
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How should you design tools?
Tools are the agent’s interface to information and actions, so their boundaries should be legible to both the model and the people responsible for the system. OpenAI groups tools into data tools for retrieval, action tools for changing systems, and orchestration tools for invoking other agents. Its agent guidance recommends standardized, well-documented, tested, reusable definitions.
- Give each tool a distinct, descriptive name.
- Use clear descriptions and parameters that make the tool’s purpose and limits apparent.
- Resolve overlap between tools so the agent has a sound basis for choosing one.
- Keep available tools scoped to the task; clarify their boundaries before adding another agent.
- Separate tools that retrieve information from tools that change systems, and place approval before consequential actions.
How can you improve an underperforming agent?
Start with the observed failure rather than making a broad prompt change. Inspect the trace of decisions and tool calls, then adjust the component that caused the problem.
It chooses the wrong tool
Review tool names, descriptions, parameters, and overlap. Clarify the tool boundaries or narrow the available choices before splitting the work among agents.
It cannot complete a multi-step task
Determine whether the task is actually a fixed workflow, a single agent with tools, or a job with distinct subtasks that warrant specialist roles. Where decomposition is justified, make the subtasks explicit.
It keeps looping or revising
Define an objective stopping threshold and a hard limit on iterations. Measure whether each additional pass improves quality enough to justify its latency and cost.
It takes actions the user did not intend
Make the goal and policy concrete, show planned actions, and require human approval before consequential changes. An apparently simple request, such as organizing files, can be interpreted more broadly than intended.
The team cannot tell what happened
Retain traces and evaluate decisions and tool calls. Make plans and actions visible enough for people to diagnose and redirect the system without burying them in unnecessary detail.
How should you evaluate improvement?
Measure performance against the intended outcome, not just whether the agent completed a sequence of calls. Record task success, output quality, tool errors, latency, cost, and safety failures. Use the results to identify where the design falls short and to compare changes over bounded cycles.
For iterative refinement, set a quality threshold or a maximum number of revisions so the system has a clear exit condition. Review whether quality gains justify extra calls, time, and expense. Google Cloud’s pattern guidance and Anthropic’s implementation guide discuss matching patterns and iteration to the task rather than adding complexity by default.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you keep an agent safe and under human control?
Safety belongs in the workflow design, not just in a final review. OpenAI identifies prompt injection and unintended disclosure of private data as agent risks. Its agent safety guidance recommends measures including clear instructions and examples, input guardrails, structured outputs to constrain data flow, and approvals for tool use.
- Treat external text as untrusted input; do not place untrusted variables in privileged instruction contexts.
- Constrain information passed between steps with validated structured outputs.
- Require confirmation or approval for sensitive and consequential tool operations.
- Use traces and evaluations to inspect decisions and tool calls, not only final responses.
- Keep people able to assess and redirect how the system pursues a goal, especially for high-stakes decisions.
Anthropic describes the central design tension as “balancing agent autonomy with human oversight.” Its framework for developing safe and trustworthy agents, published August 4, 2025, emphasizes visibility into reasoning and planned actions so people can assess and redirect an agent. The right level of oversight depends on the scenario. These controls reduce risk; they do not guarantee that an agent will never make a mistake or be tricked.
Quick Recap
A practical design sequence
- Define the job: Specify the desired result, success criteria, permitted data and actions, and approval points.
- Choose the simplest workable pattern: Use a fixed workflow or one model call for predictable work; otherwise begin with one agent.
- Make tools clear and bounded: Give them distinct names, well-defined parameters, and documented limits; remove ambiguity and unnecessary access.
- Add specialists only for distinct subtasks: Choose a manager or peer handoffs according to who needs to control the workflow and synthesize the result.
- Evaluate and refine: Inspect traces, measure task and safety outcomes, and cap any revision loop with a quality threshold or iteration limit.
- Keep human oversight where it matters: Expose planned actions and require approval before consequential changes.
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