Guide How AI Agents Work: Build and Test the Core Agentic Cycle

This guide is for developers, technical beginners and business teams who want to understand how a single AI agent works and build a basic agent workflow. You do not need to create a complicated application to follow the process.

By the end, you will have designed a single agent that accepts a goal, gathers information, chooses an action, uses a tool and checks the result. You will also know how to recognise common failures and decide when an agent should stop or ask for help.

The central idea is the agentic cycle. An AI agent repeatedly observes its environment, decides what to do, takes an action and evaluates the outcome. The cycle continues until the task is complete, a stopping condition is reached or human input is required.

This is different from a basic chatbot that produces an answer from the current conversation. An agent can use tools to gather information and change its next action based on what those tools return. However, tool access does not guarantee accurate reasoning, successful execution or safe decisions.

What you will need

• A clear task: Choose a goal with an outcome you can verify, such as collecting travel options or organising information from documents.

• Access to an AI model: Use a model that can follow instructions and, if you plan to automate execution, work with structured tool calls.

• A simple workspace: A text editor is enough for designing the workflow. A programming environment is useful if you want to connect real tools.

• A source of information: This could be a sample dataset, a document collection, an approved web search service or another application interface.

• A way to inspect results: Keep a record of tool requests, responses, errors and final outputs.

• Permission boundaries: Decide which information the agent can access and which actions require human approval.

We will use a travel-planning example throughout the guide. The agent's goal is to find suitable flights and accommodation for a trip to Tokyo while staying within a user-defined budget.

The example is illustrative. We have not connected a live travel service or tested a particular agent product, so it does not contain verified flight availability, hotel ratings or booking results.

Step 1: Define the goal and success conditions

Start by writing down exactly what the agent should accomplish. A vague instruction such as 'Plan my holiday' gives the agent too much room to make assumptions.

A better instruction is: 'Find suitable flights and accommodation for my Tokyo trip. Compare the available options, stay within my budget and present the results for approval. Do not make a booking.'

This tells the agent what to investigate, what to produce and where its authority ends.

Next, identify the information required to complete the task. For travel planning, this includes the departure city, travel dates, number of travellers, budget and accommodation preferences.

Define success before the agent begins. The result should contain flight and accommodation options that match the user's constraints, a clear explanation of any missing information and a recommendation supported by retrieved data.

If the agent cannot find suitable options, it should report that limitation rather than inventing results.

Also define stopping conditions. The agent should stop when it has enough verified information to answer, when an essential tool remains unavailable or when the next action requires permission.

These boundaries make the workflow easier to test and reduce unnecessary tool calls.

Step 2: Perceive the goal and gather context

The perceive stage is where the agent receives the user's request and gathers information about the current situation.

For our travel example, the agent reads the destination, dates, budget and preferences. It then checks whether the request contains enough information to begin searching.

If the departure city is missing, the agent should ask for it. If the user has already supplied the dates, the agent should retain them instead of asking again.

Perception may also involve reading documents, retrieving database records or examining information returned by connected services.

Separate the information the user supplied from information retrieved through tools. The agent should know whether a detail is a confirmed requirement, an external result or an assumption.

This matters because external content can be incomplete, outdated or misleading. A webpage might contain an instruction that has nothing to do with the user's request. The agent should treat retrieved content as data, not as permission to change its behaviour.

At the end of this stage, the agent should have a clear representation of the goal, available context, missing details and permitted actions.

Step 3: Reason and break the goal into smaller tasks

The reasoning stage determines what the agent should do next. It uses the goal and available context to select a suitable plan.

For the Tokyo example, the agent might organise its work into the following tasks:

1. Check available flights for the requested route and dates.

2. Find accommodation matching the location and budget requirements.

3. Review relevant hotel information and cancellation conditions.

4. Compare the combined travel options.

5. Prepare a recommendation for the user.

This plan creates a useful order of work. The agent can avoid searching for unrelated information and focus on the decisions needed to complete the request.

Reasoning does not mean the model has human awareness or that its internal calculations are always reliable. In practical agent systems, reasoning means using the available information to choose a next step, decide whether a tool is needed and evaluate possible actions.

A plan should remain flexible. If a flight is unavailable, the agent may need to reconsider the route or ask whether the user accepts different travel dates.

We recommend keeping early agent designs simple. Begin with tasks that have clear dependencies and outputs. Add more complex planning only when the basic workflow behaves reliably.

Step 4: Connect the agent to useful tools

An agent becomes more useful when it can interact with external systems instead of relying only on information already available to the model.

For travel planning, useful tools might include a flight-search interface, an accommodation database and a calculator for comparing totals.

Other agent workflows may use web search, document retrieval, database queries or code execution.

Each tool should have a defined purpose, clear input requirements and a predictable response format.

For example, a flight-search tool might accept a departure airport, destination, travel dates and passenger details. It should return structured results that the agent can inspect.

Before enabling a tool, decide whether the agent needs read-only access or permission to modify information.

A travel agent that only compares options does not need permission to purchase tickets. A document research agent does not automatically need permission to delete files.

Apply the same rule to database access, email tools and external applications. Grant only the permissions required for the assigned task.

When implementing tool calls, validate the inputs before execution. Check that required fields are present and that the requested operation falls within the agent's permissions.

Tool responses should also be checked before they influence the next action. An empty response is not evidence that no options exist, and an error message is not a successful result.

Step 5: Act by executing the planned tasks

The act stage turns the plan into operations.

In our example, the agent sends a request to the flight-search tool using the user's travel details. It receives the results and selects the information needed for comparison.

The agent then queries the accommodation source and gathers suitable hotel options. It may use a calculation tool to compare totals against the user's budget.

The important distinction is that the agent must receive and inspect actual tool responses. A model-generated statement claiming that a search was completed is not proof that the tool ran successfully.

Record the tool name, requested operation, response status and relevant output. This makes it easier to investigate failures.

If a tool requires authentication, the application should handle credentials securely. Avoid placing passwords or secret keys directly in prompts or exposing them in logs.

For actions that affect external systems, such as sending an email, updating a database or completing a purchase, add an approval step when appropriate.

In our travel example, the agent can recommend a booking, but the user must approve any purchase. This keeps the agent's authority aligned with the original goal.

Step 6: Reflect on results and adapt the plan

Reflection is the stage where the agent checks whether its actions produced the expected outcome.

Suppose the flight-search tool returns no suitable flights. The agent should not immediately conclude that travel is impossible.

It should inspect the result and consider possible causes. The requested route may be unavailable, the search parameters may be incomplete or the service may have returned an error.

The next action depends on the cause. If the tool failed temporarily, the agent may retry within a defined limit. If the route is unavailable, it may search for alternatives permitted by the user's preferences.

If an alternative requires changing the travel dates, the agent should ask for approval rather than silently modifying the user's requirements.

Reflection also applies to successful tool calls. A search may return results that are technically valid but unsuitable because they exceed the budget or conflict with the user's constraints.

The agent should compare each result against the original goal before deciding what to do next.

This creates the central feedback loop: perceive new information, reason about it, act and reflect again.

Reflection is not the same as permanent learning. An agent can adjust its current plan without updating the underlying AI model. Persistent memory or model training requires separate mechanisms and controls.

Set limits on retries and repeated planning. Without stopping conditions, an agent may repeat the same unsuccessful action and waste computing resources.

Step 7: Combine the agentic cycle into a working loop

Once the individual stages are clear, connect them into a repeatable workflow.

The basic process is straightforward:

1. Receive the user's goal and available context.

2. Check for missing information and permission restrictions.

3. Choose the next task and the tool needed to perform it.

4. Execute the tool call and inspect the response.

5. Evaluate whether the result advances the original goal.

6. Update the plan if necessary, or stop when the task is complete.

Keep the user's requirements available throughout the process. The agent should not lose important constraints after receiving new information.

Maintain a clear distinction between completed tasks, pending tasks and failed actions. This prevents the agent from presenting unfinished work as a successful result.

A simple implementation can store the goal, current plan, tool responses and task status in an application-managed state object.

The model can then receive the relevant state when deciding its next action. The application remains responsible for validating tool calls, enforcing permissions and applying stopping conditions.

This separation matters because a model's proposed action should not automatically become an authorised action.

Step 8: Test the agent with normal and failed tasks

Testing should cover more than whether the agent produces a convincing final answer.

Begin with a complete request containing all required information. Check that the agent follows the expected sequence, uses the appropriate tools and produces an answer supported by the returned data.

Next, remove an essential detail. The agent should ask for clarification instead of inventing the missing information.

Then simulate a failed tool request. The agent should recognise the error, decide whether a retry is appropriate and avoid claiming that the operation succeeded.

Also test conflicting information. If a hotel listing and a booking response disagree, the agent should explain the conflict or retrieve more reliable information before recommending an option.

Finally, test a request that exceeds the agent's permissions. For example, ask it to complete a booking when its instructions allow recommendations only.

The correct behaviour is to stop before the purchase and request approval.

We recommend recording expected outcomes before running each test. Compare the actual behaviour with those expectations, including tool usage, permission checks and error handling.

We have not run these tests on a particular product. They are practical checks for readers implementing their own agent workflows.

Common mistakes

Giving the agent an unclear goal

Broad instructions encourage assumptions and make success difficult to measure. Define the required output, constraints and conditions for completion before execution.

Providing too many tools

Unnecessary tools increase the range of possible mistakes and permission risks. Start with a small set of tools directly related to the task.

Trusting every tool response

External services can return incomplete, incorrect or malicious content. Validate results and keep untrusted information separate from instructions and permissions.

Allowing unlimited retries

An agent may repeat failed operations without making progress. Set retry limits, recognise repeated errors and define when human assistance is required.

Confusing reflection with learning

Revising a plan during execution does not mean the model has permanently learned a new skill. Persistent memory and model updates need their own implementation and evaluation.

Skipping human approval

Some actions have consequences outside the application. Require confirmation before purchases, destructive operations or other sensitive changes.

Measuring success by the final answer alone

A polished response can hide incorrect tool calls or invented results. Review the execution record and confirm that the underlying operations actually succeeded.

Check it worked

Your single-agent workflow is ready for further development when you can demonstrate the full cycle using a task with a verifiable outcome.

Use this checklist:

• Perceive: The agent identifies the goal, records constraints and asks for essential missing details.

• Reason: It creates a relevant plan and selects actions that contribute to the goal.

• Act: It calls authorised tools with valid inputs and processes their actual responses.

• Reflect: It checks results, recognises failures and changes its plan when appropriate.

• Permissions: It refuses or pauses actions that exceed its authorised scope.

• Completion: It stops when the requested outcome is achieved or clearly explains why it cannot continue.

• Evidence: Its final response matches the information returned by the tools.

For the travel example, a successful result would be a clear comparison of available options that satisfy the user's requirements, supported by retrieved information and presented without making an unauthorised booking.

If the agent cannot find suitable options, a correct explanation of the limitation is better than a fabricated recommendation.

The core engine of a single AI agent is therefore a controlled decision-and-action loop. It observes, plans, acts and evaluates what happened before continuing.

Once this basic workflow is reliable, you can consider more advanced features such as persistent memory, additional tools and coordination between agents. We recommend improving the accuracy, permissions and error handling of a single agent before adding that complexity.