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03 · Partners · Guide

AI Agency Checklist: 5 Questions to Ask a Multi-Agent Provider

The market for AI service providers is hard to see through. Use these questions to check the technical depth of potential partners.

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Why a Checklist?

Multi-agent systems are more complex than classic software. Asking about "AI experience" is not enough. You want to know whether a provider solves architecture problems before they hit you in production.

The 5 Questions for Your First Meeting

1. Architecture: Which framework do you use for orchestration, and why?

Why it matters: Without a deliberate orchestration layer, multi-agent systems quickly become impossible to maintain.

Red flag: "We build everything ourselves" without a reason, or a single framework for every case.

Good answer: "LangGraph for strictly defined workflows, the OpenAI Agents SDK or Microsoft Agent Framework when handoffs between agents matter; tools connected via MCP."

2. Errors: How do you prevent endless loops and runaway costs?

Why it matters: An agent that fails and retries again and again can burn through a budget in hours.

Red flag: "That doesn't happen" or no concrete answer.

Good answer: "Step and cost limits per run, timeouts, retries with backoff and a supervisor that stops the run and alerts someone."

3. Quality: How do you measure whether the system gives correct results?

Why it matters: Language models still make things up. Without systematic evaluation nobody notices until a customer does.

Red flag: "The new models no longer hallucinate."

Good answer: "Test sets from real cases, automated evaluation in CI, tracing in production, grounding via RAG and structured outputs."

4. Memory: Where is the state of the agents stored?

Why it matters: Multi-step processes have to survive restarts and hand context between agents.

Red flag: "In the session" or no clear answer.

Good answer: "Persistent checkpoints in a database, separate long-term memory, clear retention periods."

5. Security: How do you stop agents from seeing data they shouldn't?

Why it matters: With access to your knowledge base, an agent must not reveal the CEO's salary to an intern.

Red flag: "We filter that in the prompt."

Good answer: "Access rights are enforced at retrieval level, every tool runs with the user's permissions, all actions are logged."

Bonus: Can I see a system in production?

Ask for concrete systems that run live, not demos. Ideally you can talk to a customer who has been operating the system for some time.

Conclusion

Providers who answer these questions concretely can build systems that go beyond a prototype. In our directory you can see for each entry whether and when we checked the basic details; the technical depth is something only you can check in conversation.

Next step

From comparison to project.

Describe your project in three steps. We review it and name up to three suitable partners to you by email before anything is passed on. Free for you.

FAQ

Frequently asked questions

Why isn't asking about 'AI experience' enough?

It is too general. Multi-agent systems need specific skills in orchestration, state management, evaluation and permissions, which many chatbot projects never required.

What if the provider can't answer a question?

Ask them to follow up in writing. If the answer stays vague, production experience is probably missing.

Should I ask about specific frameworks?

Yes. A good provider can explain why they would pick LangGraph, Microsoft Agent Framework, OpenAI Agents SDK, Google ADK or CrewAI for your case, and what that means for hosting and lock-in.