The Tool Desk
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To build a custom AI agent, choose a workflow that benefits from judgment, give an LLM a defined set of tools and instructions, and evaluate the agent on representative tasks before putting it into production. Start with one agent and a narrow job; add complexity only when testing shows it improves results. A chatbot that only answers questions is not necessarily an agent—the defining feature is that the system controls a workflow toward completion, including taking actions, recognizing failure, and handing control to a person when needed.
What is an AI agent, and how is it different from a chatbot?
An AI agent uses a large language model to manage the steps of a task, decide when to use tools, and determine whether to continue, stop, or ask for help. OpenAI defines agents as systems that independently accomplish tasks in its A Practical Guide to Building AI Agents, published January 15, 2026. Independence does not mean unrestricted authority: a production agent should act only within its assigned permissions and guardrails.
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A chatbot may generate an answer in response to a prompt. A single-turn LLM call or a classifier may be useful software, but it is not an agent unless it controls workflow execution. The distinction matters when choosing what to build: if the job ends with producing text, a simpler assistant may suffice; if it must gather context, make decisions, take permitted actions, and handle exceptions, an agent may fit.
The agent’s basic run loop
- Receive a task. The system gets a request or a workflow item and the context it is allowed to use.
- Decide what to do next. The model interprets the instructions and chooses a step, such as retrieving information, asking a clarifying question, or preparing an action.
- Use a tool when needed. A tool returns data or performs an authorized action. The agent uses the result to decide what happens next.
- Check the outcome. The agent should be able to recognize completion, respond to a failed or incomplete step, stop when it cannot proceed safely, or transfer control to a person.
The model supplies flexible decision-making; the workflow, tools, and guardrails define what that flexibility can do.
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When should a team build an agent?
Consider an agent when a workflow involves complex decisions, brittle rules that are difficult to maintain, or substantial reliance on unstructured information. If stable, deterministic rules already handle the job reliably, conventional automation may be simpler and more appropriate.
Examples suited to evaluation as agent workflows include nuanced refund decisions, vendor security reviews, and insurance-claim processing. These examples are not a guarantee that an agent will outperform existing processes. Each team needs to compare the agent with the current workflow on the same representative cases.
Use this workflow-fit check
- Decision complexity: Does the task require interpreting context or handling meaningful exceptions, rather than applying a short set of fixed rules?
- Information shape: Does the work depend on documents, messages, or other unstructured inputs?
- Action boundary: Can you state which systems the agent may read or change, and which actions need approval?
- Evaluation: Can the team assemble representative cases and define what counts as a correct, safe result?
- Operational value: Is there a clear reason to automate this workflow, such as reducing manual handling or making complex work easier to manage?
If the team cannot define the permitted actions or judge whether outcomes are acceptable, it is not ready to delegate the workflow to an agent. Begin by clarifying the process and its decision criteria.
What are the minimum parts of a custom AI agent?
A minimum viable agent has three foundations: a model, tools, and explicit instructions. OpenAI’s January 15, 2026 guide describes these as the core components. The product experience also needs a clear run loop: a way to start a task, interpret tool results, recognize completion or failure, and hand off when appropriate.
1. Model: the decision-making component
The model interprets instructions and context, chooses among available next steps, and determines when a tool is useful. Model choice should be tested against the actual workflow rather than made from a general ranking. Establish a quality baseline with the strongest practical model available to the team, then test smaller models against the same evaluation cases. Keep a smaller model only if it meets the required accuracy while improving cost or latency.
2. Tools: the agent’s permitted capabilities
Tools connect the agent to information and actions. OpenAI groups them into three types:
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- Action tools update systems or communicate on the agent’s behalf.
- Orchestration tools allow an agent to call another agent or hand work to one.
Give each tool a clear purpose and a well-defined boundary. Document what it accepts, what it returns, what permissions it requires, and what failure looks like. Test tools independently as well as in the full workflow, and make them reusable and versioned so changes can be reviewed.
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3. Instructions: the operating rules
Turn existing procedures, support scripts, and policy documents into explicit instructions rather than expecting the model to infer an organization’s process. Break a dense job into smaller steps. For each step, specify the action, the information needed, the expected output, and what to do if information is missing or a case falls outside the usual path.
Instructions are not a substitute for technical controls. If an action must be restricted, enforce the restriction in permissions and tool design as well as describing it to the model.
How do you build an agent step by step?
Build from a small, testable workflow toward production. Keep the first version narrow enough that the team can understand its decisions, inspect tool calls, and diagnose failures.
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- Map the existing process. Collect the relevant operating procedures, support scripts, and policies. Write down the normal path, edge cases, missing-information cases, and actions that require human judgment or approval.
- Define the agent’s authority. Separate information it may read from systems it may change. Identify actions it can perform automatically, actions requiring approval, and actions it must never take. Ensure system permissions match those boundaries.
- Write the run loop and instructions. Specify how the task starts, how the agent chooses a step, how it handles tool results, how it recognizes completion, and when it stops or transfers control. Make each step and its expected output clear.
- Build the smallest useful tool set. Add only the data and action tools needed for the workflow. Document and test each tool, including its error behavior. Avoid a broad collection of vaguely described capabilities.
- Create representative evaluations. Assemble cases covering routine work, difficult decisions, edge cases, missing information, and unsafe or unauthorized requests. Define acceptable outcomes before optimizing the model or expanding the workflow.
- Establish a baseline with a strong practical model. Run the evaluation cases and inspect both the final results and the paths that produced them. Use this baseline to identify weaknesses in instructions, tools, or workflow design.
- Improve one part at a time. Fix unclear instructions, missing context, tool behavior, or permission boundaries that cause failures. Add tools incrementally so each change can be evaluated and maintained.
- Test smaller models only against the baseline. Compare them on the same cases. A lower-cost or faster option is a valid choice only if it still meets the workflow’s accuracy and safety requirements.
- Review production readiness. Verify security, human approval, monitoring, limits, memory handling, rollback, incident response, and accountable ownership before launch.
- Operate and revise the system. Review failures and changes to the underlying workflow. Re-run evaluations when instructions, tools, models, or permissions change.
Should you build one agent or multiple agents?
Start with one agent and a clear run loop. Multiple agents add coordination and additional failure paths, so use them when there is evidence that separation improves performance or scalability—not as a default architecture.
When one agent is the better starting point
- The workflow has a coherent objective and a manageable set of tools.
- The instructions can describe the process without an unwieldy number of conditional branches.
- The team needs to learn where the system fails before adding orchestration.
When to consider multiple agents
Consider splitting the work when prompts have many conditional branches, the tool set becomes confusing or overloaded, or specialized agents demonstrably improve performance or scalability. OpenAI describes two common patterns:
- Manager and specialists: A manager agent calls specialist agents as tools and remains responsible for coordinating the task.
- Decentralized handoffs: Agents transfer work to one another as the workflow progresses.
These patterns create different control structures. A manager pattern keeps coordination in one place; decentralized handoffs distribute it. In either case, specify which agent owns each step, what information passes between agents, which one may take an action, and what happens if a handoff fails. Compare the multi-agent version with the single-agent baseline using the same evaluations.
How do you make an AI agent reliable and safe in production?
Reliability is a lifecycle responsibility, not a final prompt edit. Microsoft’s adoption framework groups the work into plan, govern and secure, build, and manage. Its multi-agent reference architecture also calls out memory, communication, observability, evaluation, security, and governance. Google Cloud’s production guidance emphasizes dedicated approaches to testing, memory, orchestration, and security because agents reason, act, and adapt.
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Evaluate behavior before launch
Use representative cases that reflect the real task, including unusual but plausible inputs and cases where the correct result is to ask for help or stop. Evaluate the outcome and the path: whether the agent used appropriate tools, respected permissions, handled missing information, and reached a defensible completion or handoff. Re-run the cases after material changes to the model, instructions, tools, or workflow.
Protect tools, data, and identities
Set authentication and authorization for the systems the agent touches. Give tools only the access needed for their purpose, and treat retrieved content as potentially untrusted. Include defenses against prompt injection and data exfiltration: a request or document should not be able to override the agent’s authority or cause it to reveal information outside the task’s permissions.
Put people in the consequential paths
Define which decisions can be completed autonomously and which require human approval. Make the handoff a real workflow state, not just a sentence in the agent’s response: the person taking over needs enough context to review the case and decide what happens next. The appropriate approval boundary depends on the consequences of an incorrect action; the cited guidance does not establish one universal threshold for every product.
Observe execution and limit runaway work
Monitor traces and costs so the team can understand what the agent did and investigate unexpected results. Set operational limits for retries, timeouts, and maximum turns. These controls help prevent a stalled task from consuming resources indefinitely and give the system a defined point at which to stop or escalate.
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Manage memory and incidents
Decide what the agent needs to retain, how long it should retain it, and how people can request deletion. Plan for incidents, rollback, and ongoing ownership after launch. Memory and state are architecture choices: keep only what the workflow needs, and ensure their retention and deletion behavior is understood before use.
There is no single threshold for evaluation scores, retries, timeouts, or approval requirements that applies to every agent. Set those controls for the product’s risk and operating conditions, then verify them through testing and monitoring.
How should you choose an agent framework or platform?
Compare options against the workflow and the team’s operating needs rather than choosing by feature count. OpenAI’s build guidance and the Microsoft and Google production materials point to a practical set of dimensions; the right priorities change between a small first workflow and an enterprise deployment.
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| What to compare | Question for the team | Why it matters |
|---|---|---|
| Workflow fit | Can the platform represent the task’s decisions, tools, handoffs, and completion conditions? | A poor fit can make instructions and orchestration harder to maintain. |
| Model quality | Can the team test candidate models on its own representative cases? | General capability does not establish performance on the team’s task. |
| Tools and connectors | Does it support the systems the workflow needs, with suitable permission boundaries? | Integration convenience must not come at the cost of excessive access. |
| Latency and total cost | Can the team measure response time and operating cost for the full workflow? | Model choice alone does not describe the cost or speed of a task involving tools and retries. |
| Security and identity | Can the team control authentication, authorization, and access to data and actions? | Agents need enforceable boundaries around what they can read or change. |
| Observability and evaluation | Can the team inspect traces and repeatedly test behavior? | Without visibility and repeatable checks, failures are harder to diagnose. |
| Human approval | Can the workflow pause for review and resume after a decision? | Some actions should not proceed without an accountable person. |
| Memory and state | Can the system manage the state the workflow requires, including retention and deletion? | State affects continuity, privacy, and operational control. |
| Portability and lock-in | How difficult would it be to move models, tools, or deployment elsewhere? | Convenience today can create switching costs later. |
| Governance and auditability | Can the organization manage ownership, oversight, and review of agent activity? | These controls become especially important in larger deployments. |
For a small first workflow, implementation speed and evaluation quality usually deserve the most attention. For enterprise deployment, governance, identity, auditability, and operating controls become decisive. Google Cloud’s 2026 platform overview describes infrastructure for building, deploying, scaling, governing, and optimizing agents, including long-running state and an Agent Development Kit; those capabilities are relevant only if they fit the team’s requirements and deployment environment.
What should a launch checklist cover?
Use a written review so the build, security, and operations teams can verify responsibilities before the agent handles real work.
- Task and evaluation: Is the workflow bounded, and are representative cases and acceptable outcomes documented?
- Permissions: Are authentication and authorization configured, with tool access limited to the required systems and actions?
- Untrusted inputs: Have prompt-injection and data-exfiltration risks been considered for retrieved content and user requests?
- Human oversight: Are consequential actions routed for approval, and can the person taking over review the context?
- Observability and limits: Can the team inspect traces and costs? Are retries, timeouts, and maximum turns defined?
- Memory: Are retention and deletion behavior specified?
- Operations: Is there an incident response plan, a rollback path, and a named owner after launch?
- Change management: Will the team re-evaluate behavior when models, instructions, tools, or permissions change?
These are categories to address, not universal numeric settings. Set specific limits and approval rules according to the product’s risks, then test that the system follows them.
What are the most common ways an agent project goes off track?
Many avoidable problems come from giving the model a broad task before defining the workflow, tools, and evidence of success.
Starting with a multi-agent design
More agents mean more handoffs and more places for context or responsibility to become unclear. Begin with one agent, then split responsibilities only when evaluations show a measurable benefit.
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Adding tools before the process is clear
A large tool set can make it harder to understand why the agent chose an action. Document a small set of reusable tools and add capabilities only when the workflow needs them.
Optimizing cost before establishing quality
A cheaper or faster model is not an improvement if it misses important cases. Establish a baseline with a strong practical model, then test smaller options against the same target.
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Relying on instructions to enforce security
Text telling an agent not to take an action is not the same as a permission boundary. Enforce access restrictions in authentication, authorization, and tool design, and evaluate behavior against untrusted inputs.
Launching without an owner or recovery path
An agent needs someone responsible for monitoring, incident response, and rollback. If no one owns changes after launch, failures can persist even when the system’s behavior or the underlying workflow changes.
Frequently Asked Questions
Do I need a large engineering team to build an AI agent?
Not necessarily: the first useful version can be a narrow workflow with one agent and a small tool set. The work still needs clear process ownership, someone able to configure integrations and permissions, and people who can judge whether evaluation results are acceptable.
Can an AI agent run for a long time without a person?
Some platforms support long-running state, but that capability alone does not establish that a workflow is safe to leave unattended. Define stop conditions, operational limits, monitoring, and escalation before allowing extended execution.
Should an AI agent be allowed to change customer or business records?
Only when the workflow explicitly requires it and the tool permissions, review policy, and recovery plan support that authority. For high-consequence changes, design an approval step rather than relying on the agent’s confidence.
What happens when an agent cannot finish a task?
A well-designed workflow should treat inability to proceed as an expected outcome, not force a guess. The agent can stop, explain what is missing, and transfer the case with enough context for a person to continue.
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