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03 · Partners · Guide

Multi-Agent PoC vs. Production: Partners for the Step from Prototype to System

Many agent projects stall after the proof of concept. What changes on the way to production and what you should expect from a partner.

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The PoC Paradox

A demo agent that works in nine out of ten cases is quickly built. A system that runs reliably every day, with changing models and real data, is a different kind of work. That is why an impressive PoC so often turns into a disappointing rollout.

The 5 Biggest Differences Between PoC and Production

1. Error handling

PoC: "If it breaks, we restart it."

Production: "What happens when a model API is down or slow? How do we recover, and who gets alerted?"

What you need: Retries with backoff, fallback models, circuit breakers, alerting.

2. Load and cost

PoC: A handful of test requests.

Production: Real volume with fluctuating load and a budget.

What you need: Queues and asynchronous processing, caching, step and token limits, cost monitoring per run.

3. Prompts and models

PoC: Prompts hard-coded, one model.

Production: Prompts and models change regularly, and every change can affect quality.

What you need: Versioned prompts, test sets from real cases, automated evaluation before every release, rollback.

4. Observability

PoC: "It works on my laptop."

Production: "Which agent made which decision, how long did it take and what did it cost?"

What you need: Tracing across all agents and tool calls (for example with LangSmith, Langfuse or OpenTelemetry), latency and token tracking.

5. Security and compliance

PoC: "We call the model API directly."

Production: "Where is data processed, who may see what, and how do we document it for the GDPR and the EU AI Act?"

What you need: EU hosting or on premises where required, permissions per tool, data minimisation, audit logs.

What to Expect from a Production Partner

You need partners who take software engineering as seriously as AI. Ask about:

  • CI/CD: automated tests and evaluations on every change
  • Infrastructure as Code: reproducible environments
  • Monitoring: tracing, cost and quality dashboards
  • Incident handling: agreed response times, runbooks, post-mortems

References Are Critical

Ask for concrete systems that run live and for numbers:

  • "How many runs does the system process per day?"
  • "How do you measure quality, and how has it developed?"
  • "What did a model change cost you last time?"
  • "How quickly do you respond to incidents?"

Finding Partners

In our directory you can see for each entry whether and when we checked existence, location, website and agent offering. Whether a provider can run systems in production is something you find out with the questions above.

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.

Request a projectBrowse the directory

FAQ

Frequently asked questions

Why do many AI projects stall after the PoC?

A PoC answers "does it work?". Production asks "does it work reliably, securely and at an acceptable cost?". That requires error handling, evaluation, monitoring and clear responsibilities, which a PoC usually skips.

Can the same provider do PoC and production?

Ideally yes, but not every PoC specialist has operations experience. Ask explicitly for systems that have been running live for months.

How long does the step from PoC to production take?

That depends heavily on integrations, data quality and approvals. Ask for a plan with milestones and clear criteria for when the system counts as ready for production.