AI agents: automate a workflow while staying in control

Define an agent without giving it magical powers

An AI agent combines a model, tools and an execution loop. He receives a goal, chooses an authorized operation, observes the result and then decides whether to continue or respond. The model proposes action; the application remains responsible for its execution. This distinction is fundamental: a text produced by the model is not an authorization. The system must verify identity, settings, and business rules with every operation, even if the agent has already completed several correct steps in the same conversation.

Choosing between explicit workflow and agent

If all the steps are known and stable, a classic workflow is often simpler to test. For example, a validated request always triggers the same ticket creation and the same notification. An agent becomes interesting when the path depends on ambiguous information: searching for a customer under several names, consulting different sources or asking for clarification. Avoid replacing a deterministic sequence with a free loop just because the word agent is popular. We can combine the approaches: interpretation by the model then explicit orchestration of the sensitive stages.

Design narrow and understandable tools

A tool should describe a limited capacity, such as researching a client or preparing a draft. Define typed parameters, required fields, and explicit errors. A generic tool that can execute any query or command unnecessarily widens the risk. Separate reading and writing and avoid responses with more data than necessary. The tool should check permissions regardless of the model. Its documentation must also specify the consequences: creating a draft file does not have the same impact as publishing an order or sending a message to a customer.

Complete example: qualify a commercial request

A business receives an email requesting a customer portal. The agent extracts the functionalities mentioned, finds the company in the CRM then searches for recent exchanges. It offers an opportunity sheet with confirmed information and indicates what is missing: budget, date or number of users. The salesperson validates the form and the draft response. If two companies have the same name, the agent asks for clarification instead of choosing one arbitrarily. The useful result is a prepared and verifiable file, not a prospecting sent automatically without control.

Framing memory and task state

Conversation memory is used to preserve the context of an exchange; the business state belongs to the reference system. A sentence saying that a payment is accepted should not replace checking the status in the payment service. Store resource IDs and useful results, without retaining all sensitive data indefinitely. Distinguish between confirmed facts, hypotheses and preferences. Define what survives a new session. This separation prevents an old incorrect answer from becoming a so-called truth simply because it has been memorized.

Provide human validations in the right places

Validations must match real-world consequences. A reading can be automatic; a deletion, payment or external message deserves explicit control. Present the person with the recipient, the data and the exact action, not just a general question asking if they agree to continue. After validation, double-check that the resource has not changed significantly. A change in amount or recipient may invalidate the previous agreement. Keep a record of the decision to explain who authorized what and in what context.

Make retakes safe and limit loops

Network calls may time out after the action has already been performed. Without an idempotency mechanism, a new attempt creates two tickets or two orders. Use a stable operation key and check the results before trying again. Also limit the number of steps, total time and budget. When a limit is reached, the agent must explain what has been completed and what remains stuck. A loop that continues to reformulate the same search is not proof of intelligence: it is often a fault of orchestration or an instruction that is too vague.

Test for failures and manipulation attempts

Test for unavailable tools, empty responses, conflicting data, and unauthorized requests. Add malicious content to received documents and messages to verify that they cannot modify application rules. A client can write that he is an administrator; this statement does not change its permissions. Evaluate complete courses and not just the final text. The agent can give a perfect response after performing a prohibited reading: the apparent success should not mask a security failure.

Observe operations and calculate profitability

Log useful steps, tools invoked, errors and validations, hiding secrets. Measure the proportion of tasks completed without correction, the number of human interventions and the cost per accepted result. Compare with previous time spent, including supervision and retakes. Automation that increases the control load may not be cost-effective. The traces must make it possible to understand a decision without requiring us to retain the supposed internal reasoning of the model; actions and their observable evidence provide a more reliable basis.

Start with a limited and evolving mission

Choose a single mission, a few tools and a pilot group. Write down the conditions for success and transfer to a person. Start by preparing actions before authorizing their automatic execution. Tuplify Studio can connect an agent to your CRM, APIs and internal procedures, with tailored validations and traceability. The autonomy can then be increased for well-controlled operations, based on the measured results. It must remain a design decision linked to business risk, and not an abstract objective consisting of eliminating all human intervention.

Workshop: define a tool contract before creating the agent

Take the operation to prepare a CRM opportunity. Define the allowed parameters, their origin and the necessary permissions. Indicate whether the tool creates a draft or a file visible to the entire team. Specify how it reacts to a customer not found, two possible outcomes or a repeated request. Add a correlation key and a result that distinguishes success, action already performed and error. Test this tool directly before making it available to the model. If the contract is not reliable without AI, the agent will not be able to make it reliable with a better instruction. The apparent intelligence of the orchestration must be based on operations whose effects and limits are controlled.

Frequently asked questions: autonomy and number of agents

Are several specialized agents needed? A single, well-defined agent may be enough. Multiple agents add coordination, cost and difficulty of diagnosis; they must respond to a measured need.

Can we delete all validations? Some readings or reversible operations can be automated. For significant consequences, set explicit, verifiable permission rather than blanket autonomy.

What to do if a task is interrupted? Maintain an observable state: actions completed, credentials created, and next step. When you resume, check what was actually executed. The conversation alone should not be the record of operations, as it may lack information or contain an erroneous assertion of the model.