The first step in building an AI agent is to define one specific workflow and decide what a successful result looks like. Write down the task, its trigger, inputs, permitted actions, constraints, and the points that require human review. Then check whether the workflow actually needs an agent: a fixed process or one model call may be simpler and sufficient.
Define the job before choosing technology
Describe the recurring job in one sentence. For example: “When a support request arrives, gather the relevant account details and draft a response for a staff member to review.” That is more useful than starting with a model, framework, or a broad ambition such as “automate customer support.”
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Make the intended outcome observable. “Help with requests” is difficult to evaluate; “produce a draft that includes the answer, cites the account information it used, and flags unresolved questions” gives a builder and reviewer something concrete to check.
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- Workflow: What recurring job should be handled?
- Trigger: What event or request starts it?
- Inputs and systems: What information is needed, and where does it live?
- Steps and tools: What must the system retrieve, and what actions may it take?
- Successful outcome: What output or completed action counts as success?
- Constraints: What must it avoid, and what should it do with missing or unexpected information?
- Human involvement: Which decisions or actions need review, approval, or escalation?
- Tradeoffs: What limits apply to response time, quality, and inference cost?
- Evaluation examples: Which ordinary and difficult cases will show whether it works?
This is a practical synthesis of questions in Google Cloud’s agentic AI architecture guidance and OpenAI’s practical guide to building agents, not a required vendor template.
Check whether the workflow needs an agent
An AI agent is not automatically the right choice for every task involving a language model. An agent is worth considering when the work requires decisions across multiple steps, use of tools or external information, or interpretation of unstructured inputs. It may also help when conventional automation has struggled with complex judgment or rules that are difficult to maintain.
Compare the three plausible approaches against the success criteria in your brief:
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| Approach | When to consider it | What to assess |
|---|---|---|
| Deterministic workflow | The steps and rules are predictable. | Reliability, maintenance burden, and handling of exceptions. |
| Direct model call | One model response can do the job, such as straightforward summarization, translation, or classification. | Output quality, response time, and cost. |
| Single agent with tools | The task requires several steps, external information or actions, and model-directed decisions. | Task completion, tool reliability, response time, cost, and human approval needs. |
There is no universal numerical threshold for choosing among these approaches. Google Cloud’s architecture guidance recommends considering whether work is predefined or open-ended, whether a model needs to orchestrate it, acceptable latency and cost, and where people need to be involved. OpenAI’s practical guide likewise recommends validating that a workflow benefits from agentic behavior rather than assuming it does. Choose the least complex approach that meets your defined requirements.
Keep the first agent focused
If an agent is justified, begin with one that owns a clear, limited task. OpenAI’s developer documentation puts the principle simply: “Define the smallest agent that can own a clear task.” See OpenAI’s agent definitions documentation. Google Cloud’s guidance also recommends starting with a single-agent system so its core logic, instructions, and tool definitions can be refined before adding more architecture.
For the first version, make the task narrow enough that a reviewer can tell whether it succeeded. Give the agent only the tools needed for that task, and produce an output that can be checked. Multiple agents are not a prerequisite. Consider splitting responsibilities only when there is a concrete reason, such as a specialist needing different tools, a different model or output style, or a separate approval policy.
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Specify instructions, tools, and boundaries
A useful initial design has three parts: a model, tools that expose external functions or APIs, and instructions that define behavior and guardrails. OpenAI describes these components in its practical guide. The brief should make clear what the agent can access, what it may do, and when it must stop rather than guess or act.
Turn existing procedures into explicit instructions where possible. Break the work into clear steps, state the expected output, and account for common edge cases such as incomplete information. Define how the agent should respond if a required detail is missing, a tool fails, or the request falls outside its assigned task.
Set approval boundaries before enabling consequential actions. OpenAI Academy’s workspace-agent guidance, dated April 22, 2026, recommends clarifying the agent’s responsibility, trigger, tools and information, process, rules, and conditions for pausing or stopping, with explicit approval for sensitive actions. For a first version, a draft or recommendation that a person checks may be more appropriate than permission to make an irreversible change.
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Test realistic cases and improve the design
Do not judge the design only on a clean, typical request. Build a small set of examples that includes routine inputs and cases with ambiguity, missing context, or unexpected details. Review whether the agent follows its instructions, uses tools appropriately, produces the required output, and stops or escalates when it should.
OpenAI Academy recommends testing straightforward and messier requests, reviewing behavior, refining instructions, and testing again in its workspace-agent guidance. OpenAI’s practical guide advises establishing a performance baseline with a capable model before evaluating whether a smaller model can meet the required accuracy at lower cost or latency. Treat model changes as something to measure against the task brief, not as an optimization to make before you know what acceptable performance looks like.
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