Build an AI agent when a task requires decisions that change with context, the system must choose how to proceed, and you can evaluate whether it reached the right outcome. Start with a restricted prototype when those conditions hold and you can bound its actions. Use simpler automation when the steps are predictable; defer when the objective or acceptable risk is still unclear.
That is a decision to test a use case, not evidence that it is ready for production. Task suitability, organizational readiness and production readiness require separate judgments.
What makes a system an agent?
OpenAI’s practical guide describes agents as systems that independently accomplish tasks on a user’s behalf. It identifies complex decisions, nuanced judgment and reliance on unstructured data as reasons to consider them.
For an engineering team, the useful distinction is who controls the next step. A workflow follows a path defined in software. An agent has discretion over how to pursue the task within its allowed boundaries. Adding an LLM to a fixed sequence does not automatically create an agent.
| Approach | How the next step is determined | A suitable task shape |
|---|---|---|
| Deterministic workflow | Explicit rules and predefined branches | Known inputs, stable rules and repeatable actions |
| LLM-assisted workflow | A predefined sequence uses an LLM for a bounded step | Interpreting text, extracting information or drafting within a fixed process |
| Agent | The model makes decisions about how to proceed as context changes | Investigation or judgment where the necessary steps vary by case |
Unstructured input alone does not settle the choice. A task might need an LLM to interpret a document while leaving routing, validation and execution under explicit rules.
Five questions to scope the task
The following diagnostic adapts the context, tool-choice and success-measurement criteria described in Olostep’s agent-building guide. Use it to expose requirements, rather than as a numerical score.
- Does new context change the necessary next step? Describe cases where a new document, answer or tool result changes the route. If the route stays fixed, a workflow may be sufficient.
- Must the system choose which action or tool to use? Identify decisions that cannot reasonably be specified in advance. A fixed sequence of tool calls is weak justification for an agent.
- Is nuanced judgment central to the work? Separate interpreting ambiguous information from executing predictable rules. Consider whether a bounded LLM step can handle the interpretation.
- Can you recognize a successful result? Write down what completion means, which errors matter and when the system should hand the task to a person. An open-ended goal without acceptance criteria is difficult to evaluate.
- Can the proposed autonomy be bounded and owned? Specify permitted actions, approval points, exit conditions and the person responsible for exceptions. A suitable task can still be organizationally unready.
The first three questions address the task. The fourth makes evaluation possible. The fifth addresses whether the team can run a controlled experiment. Favorable answers do not demonstrate reliability; they give you a testable proposal.
When to choose simpler automation or defer
Anthropic recommends starting with the simplest solution and adding complexity only when it is needed. Its guidance also identifies a trade-off between agentic task performance, latency and cost. Treat any expected improvement as a hypothesis to test against the simpler option.
Choose simpler automation when rules can describe the process, when the model’s contribution can fit into a bounded step, or when a baseline already meets the task’s requirements. Do not give the model discretion merely because the process uses natural language.
Choose a restricted agent prototype when changing context makes a fixed path inadequate, success is assessable and the team can limit the consequences of mistakes. Define the permitted scope before selecting an implementation.
Choose defer when you cannot describe the desired outcome, lack representative cases for evaluation, or have no owner for approvals and failures. Resolve those gaps before building. Otherwise, a prototype may produce convincing demonstrations without answering whether the operational task is ready.
Applying the decision to three hypothetical tasks
These examples illustrate the framework. They are not documented deployments or claims of measured performance.
Invoice processing. Suppose invoices follow known validation rules and a fixed approval route. Start with deterministic automation. If invoice text varies, consider a bounded extraction step followed by rule-based checks. An agent becomes a candidate only if resolving exceptions requires investigation and decisions that vary across cases. Test that exception-handling scope separately from the routine process.
Customer support. Retrieving relevant material and drafting a response can fit into an LLM-assisted workflow. If a support task requires investigating an issue and choosing what information to seek next, a restricted agent prototype is more plausible. Keep the success criterion concrete: what must the investigation establish, and when must it stop or escalate?
A financial action. Distinguish preparing a recommendation from executing it. A prototype might investigate and propose an action while a person retains approval over execution. If the team cannot define the action boundary and approval responsibility, defer that execution scope even if the investigation itself is suitable for an agent.
What a restricted prototype needs
Olostep describes an execution loop built around a model, tools, memory, an orchestrator and guardrails. The following conceptual diagram summarizes those roles; it is not an implementation specification.
Task and current context
|
v
Execution loop: orchestrator + model + working memory
|
v
Guardrails: permit an action, require approval, or stop
|
v
Tool action and result
|
+----> Updated context returns to the execution loop
Exit condition ----> Complete the task or hand it to a person
Diagram description: The task and current context enter a loop containing the orchestrator, model and working memory. Guardrails govern whether an action proceeds, needs approval or stops. A permitted tool action produces a result that updates the loop’s context. An exit condition ends the run or hands the task to a person.
The model supplies judgment; tools provide access to actions and information. Working memory preserves the context needed during the task. The orchestrator manages progression through the loop. This outline does not establish a need for persistent memory across tasks or for a particular framework.
Guardrail placement deserves its own design decision. Datadog’s discussion of securing agents emphasizes controls around agent actions, including approval gates and exit conditions. For a prototype, document where those controls apply and what happens when they are triggered. A statement in the task prompt is not a complete description of the action boundary.
Establish the baseline before evaluating the agent
Before expanding the prototype, compare it with the simplest credible way to complete the same task. That might be a rules-based process, an LLM-assisted workflow or the existing human process. Use the same task boundaries and acceptance criteria so the comparison answers the actual design question.
There is no universal performance threshold established by the evidence behind this article, nor a comparative benchmark proving that an agent will win for a particular use case. The following are proposed evaluation criteria, to be adapted to the task:
| Evaluation dimension | Question to answer |
|---|---|
| Outcome quality | Did the system complete the task correctly under the agreed criteria? |
| Material errors | Which mistakes would make the result unusable or require intervention? |
| Escalation | Did the system hand off cases it could not safely resolve? |
| Action boundaries | Did it attempt anything outside its permitted scope? |
| Latency and cost | Did the result justify the time and expense relative to the baseline? |
| Human effort | How much checking, correction and exception handling remained? |
Include routine cases, ambiguous cases and cases that should end in a handoff. Set acceptance criteria before reviewing the prototype’s outputs. Otherwise, the team risks accepting whatever behavior the system happens to produce.
Record failures as evidence about the design. A failure may call for clearer tools, a narrower task, a fixed workflow step or less autonomy. It does not automatically justify a more elaborate agent.
Production readiness requires operational evidence
A promising prototype establishes that the approach may work within the tested scope. Before production, require evidence that the permitted actions, approval gates and exit conditions behave as intended, including when the task cannot be completed.
As an operating recommendation, retain enough information to review the task outcome, actions attempted, approvals, handoffs and reasons for stopping. Assign someone to inspect failures and decide whether to change the system or restrict its scope. Repeat the relevant evaluation when changing tools, instructions or action permissions.
The available evidence does not establish monitoring thresholds, cost-control settings or a validated observability stack. Define those requirements for the actual deployment rather than borrowing arbitrary targets. Production approval should refer to demonstrated behavior and an explicit operating plan.
Build versus buy comes after task suitability
A managed system and a custom implementation can both be candidates once the team knows which decisions the agent needs to make. Datadog notes that the choice between self-orchestrated and managed systems affects customization and control over guardrail placement.
Assess each option against the boundaries already defined: Can you control permitted actions? Can you put approval gates where required? Can you enforce exit conditions and inspect enough behavior to evaluate failures?
These are selection questions, not claims about a particular vendor. If an option cannot support the controls your task requires, revisit the scope or the implementation choice before increasing autonomy.
A decision worksheet
Complete this worksheet with the engineering and operational owners. Keep unknowns visible rather than filling them with assumptions.
| Decision item | What to record |
|---|---|
| Task and outcome | The task boundary and observable definition of completion |
| Why an agent | Cases where changing context requires a different next step |
| Simpler alternative | The deterministic or LLM-assisted approach to compare against |
| Proposed authority | Permitted tools and actions, approval gates and exit conditions |
| Evaluation evidence | Representative cases, acceptance criteria and comparison results |
| Operating owner | Who reviews failures, handles exceptions and approves scope changes |
| Decision | Simpler automation, restricted prototype or defer |
| Unresolved evidence | What must be demonstrated before expanding scope or entering production |
If the decision is a restricted prototype, name the uncertainty it must resolve. “Determine whether adaptive investigation improves exception handling enough to justify its cost and review burden” is a testable objective. “Build an agent” is not an acceptance criterion.
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