AI Agents: The guide to designing, testing and deploying them with Copilot, ChatGPT, Claude or Mistral
AI Agent Guide
An AI agent: what it is, what it changes, and for whom
Un AI agent is an AI entrusted with a long-term role: a mission, instructions, resources, and, most often, tools to act, consult a database, send a message, fill out a form, or trigger a process. It carries out the steps independently, within the limits set for it, and reports back. The difference with a chatbot: it acts, it doesn't just respond. The difference with traditional automation: it adapts to unexpected inputs, which makes it useful and requires supervision. It exists in all environments, Copilot, ChatGPT, Claude, Mistral ou Geminiand the method for designing it is the same everywhere.
What an AI agent is not
- This is not a fixed-rule automation. A Power Automate or Zapier flow follows a pre-defined path. An agent chooses its path based on what it reads. This is what makes it valuable for variable inputs, and also why it needs to be tested on edge cases before being shared.
- It's not Cowork. A mission Cowork It is punctual and supervised live by the person who initiates it. An agent is a permanent role, with triggers and users who are not its creators. Our guide Cowork deals with the other half of the subject.
- This is not a remedy for a murky process. An agent built on a task that no one can describe produces results that no one can verify. Prioritize use cases The first skill to acquire before building is...
Which tool is right for you?
Copilot, ChatGPT, Claude or Mistral: where you build your agents depends on your environment
Each vendor now offers a workshop for building agents without coding, and a technical level for connecting them to your systems. The key question isn't "which one is the most powerful," but rather "where is your data, who manages access rights, and what has your IT department already approved?" Google addresses the same need with Gemini Enterprise, its no-code Agent Designer, and its Agent Development Kit for developers; the methodology remains the same.
How it looks: Two separate tools, not a single menu. Agent Builder, within the Microsoft 365 Copilot application, is used for question-and-answer agents built from instructions and your content (files, SharePoint sites, conversations), respecting everyone's permissions. Copilot Studio, a separate portal, is used for agents connected to your business applications: multiple steps, approvals, and autonomous triggers. An agent started in Agent Builder can be resumed in Copilot Studio.
Important point to note: Without a Microsoft 365 Copilot license, Agent Builder is limited to agents based on web sources.
How it looks: Workspace agents, available since April 2026 in ChatGPT Business, Enterprise, and Edu, bring together instructions, files, connected applications (Drive, SharePoint, Slack, etc.), skills, and scheduled tasks in an Agents tab. Sharing is done by person or group, with three access levels and the option to publish to the organization's directory; write actions require approval.
Important point to note: Custom GPTs are being discontinued. OpenAI announced on September 11, 2026, that their creation would end on October 26, 2026, and their removal on December 11, 2026 (with a possible extension to February 11, 2027 for some Enterprise spaces). The official replacement is called plugins; workspace agents are the alternative, for more agent-based use cases.
How it looks: In the conversation, there are projects with instructions and knowledge, a directory of connectors, reusable skills, and long, independent projects with approval points. Since September 16, 2026, these capabilities have returned to the main interface, Claude Cowork having been integrated into it. For technical teams, Claude Code and the Agent SDK build agents that run in your infrastructure.
How it looks: Vibe, the successor to Le Chat, combines conversation with a workflow where the assistant seamlessly guides you through steps across your connected tools (messaging, calendar, documents, Slack, GitHub, Jira), supports over a hundred connectors and the MCP protocol, and employs agents that handle requests from start to finish. Every action remains visible, and sensitive operations require validation.
From mission to mastered agent
The four keys to a useful and controlled agent
An AI agent combines a mission, instructions, data sources, and, depending on its configuration, tools to act. Its autonomy depends on the available capabilities and granted permissions. NextStart helps you define its scope, test its results, and organize its monitoring before expanding its use.
NextStart.AI Library
Choose a use case and prepare your first agent
Explore the library's job descriptions to clarify a task, its sources, and the necessary controls. Distinguish between the assistant who answers your questions, the online agent who can take action, and the tasks. Cowork in several steps. Use these resources to prepare your tests and adapt the uses to your authorized tools.
Six AI agents to be deployed in Copilot, ChatGPT, Claude or Mistral: the weekly diary and file preparer, the automated customer appointment scheduling agent, monitoring calls for tenders, the lead qualification and routing agent, the application tracking officer et the market watcher with periodic briefing.
Getting started
Build your first agent, and know what remains under your control.
A first agent within an hour
- Choose a repetitive task with varying inputs, never a process that no one can describe.
- Write his mission in four lines: what he does, in what format he responds, what he refuses, to whom he passes the reins.
- Connect only one reference source, the correct version, and nothing else.
- Test it on ten real-world cases, including three pitfalls: missing data, an out-of-scope request, and contradictory instructions. This is where trust is built.
Designing an agent that is predictable and usable by everyone →
Things to sort out before sharing it
- Distinguish between reading rights and action rights, and grant the latter only with human validation.
- Appoint an owner, write down the terms of cessation, keep a log of actions.
- Training the people who use it on what it does and its limitations: this is the meaning of Article 4 of the AI Act on the control of AI, and this is what prevents misuse.
- Repeat the tests each time the source, model, or scope changes.
Agent Finder
Which agent could you test first?
Training and deployment of agents
Learn how to design, test, and scale your agents
From the first agent tested to deployment, your teams learn to define a mission, configure instructions and sources, and then evaluate the results. The format depends on your objectives, tools, and authorized access.
Deploy the agents
Choose your pilot agents, define access and responsibilities, train your ambassadors and measure the quality and time saved before expanding usage.
Discover the support →Party, Challenge, Hackathon
Discover the design of an agent in Party, one hour build specialized agents in Half-day party ; test and prioritize your uses by Challenge, a day build custom agents by Hackathon, two days.
Discover theAgent Party® →Agent Studio
Six days spread over six weeks to take your prototypes to the stage of production agents: audit, specifications, security, connection to your tools and transfer to the teams.
Discover Agent Studio →Let's build your AI Agents program
Training for your teams or onboarding of your first agents: describe your situation in a few lines (teams involved, existing environment, deadlines). We will get back to you within 24 business hours with a tailored proposal. You can also book a 30-minute consultation directly.
Do you prefer to communicate by phone?
F.A.Q
What we are being asked about AI agents
What exactly is an AI agent?
An assistant entrusted with a long-term role: a mission, instructions, resources, and often, tools to act. They follow the steps independently within the established parameters and report back. They act, whereas a chatbot provides answers.
Agent, chatbot, automation: what's the difference?
The chatbot answers a question. The automation follows a pre-written path. The agent chooses its path based on what it reads and can act upon, making it useful for variable inputs and requiring edge case testing and ongoing monitoring.
Copilot, ChatGPT, Claude, Mistral or Gemini: where should I build my agents?
Where your data and rights reside, almost always. Microsoft 365 directs users to Copilot and Copilot Studio agents; ChatGPT Business or Enterprise to workspace agents and plugins, which are the successors to GPTs; a need for custom agents connected to your systems to Claude and its Agent SDK; and a sovereignty requirement to Mistral. The design, testing, and monitoring methodology is the same everywhere.
What are the risks, and how can they be mitigated?
Three categories: an agent that acts beyond its scope, an agent manipulated by the content it reads, and an agent whose results are no longer verified. The solutions are well-known: distinguish between read and action rights, manually validate sensitive actions, log activity, designate an owner, and retest after each change.
Do you need to know how to code?
No, not for a first agent: each vendor's workshops allow you to build it from instructions and source code. Yes, or at least a technical team, as soon as it needs to connect to your internal systems, operate in multiple applications, or run within your infrastructure.
How much does an agent cost?
Build workshops are included in publishers' subscriptions; the real cost is that of design, testing, and supervision. A poorly designed agent costs more in rework than it generates. The program or NextStart support are the subject of a proposal according to the format chosen.
What is the difference between an AI agent and Cowork ?
Cowork is a mission delegated from start to finish, which you open and close, with an expected deliverable. An agent is a permanent, equipped role, often triggered without your involvement, that handles recurring cases. One is measured by a deliverable, the other by a recovery rate. The safest path, in fact, goes from the first to the second: a mission Cowork Successfully performed and repeated three times is the best set of specifications for an agent.
What does an HR agent actually look like?
Three things, no more: a written role (what the role includes, what it doesn't include), up-to-date resources (job descriptions, agreements, interview templates, current job postings), and a human point of contact. Effective HR practices include providing reasoned feedback to candidates, preparing for interviews based on the job description and application materials, and screening applications according to written criteria. Decisions, however, cannot be delegated. See our offer. agents for recruitment.
How can an agent be made reusable by an entire team?
Four conditions separate a prototype from a team agent: a designated owner, versioned instructions and source code rather than copying and pasting from one workstation to another, a series of test cases that are replayed with each modification, and a simple measure of its cost and the amount of work it requires. Without these four elements, an agent that works for its author stops working as soon as someone else uses it.
What is the difference between an AI agent and a GPT?
If we're talking about a model, like GPT-6, it's the engine: an agent uses a model, it isn't one itself. If we're talking about a custom GPT in ChatGPT, it's an assistant configured with instructions and some documents. It becomes an agent when it receives a permanent mission, tools to act, and the freedom to complete the steps independently, within the defined limits.
Does an agent designed on one model work with another?
For the most part, the instructions, sources, and test cases are transferable, since they are written in plain language. However, each model interprets the instructions differently, and the connectors vary from one studio to another. Before switching, rerun the agent's test cases on the new model and compare the results.
How do you monitor an agent and help them develop?
Like a collaborator: an owner who regularly reviews a sample of their work, a log of errors found, and dated instructions for each modification. The rules governing all agents (who creates, who approves, who removes) fall under governance: that is the purpose of our mission. Governing your agents and our AI charter template.