AutoGen

AutoGen Multi-Agent Systems

Autonomous multi-agent conversations that solve problems too complex for a single AI model.

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Overview

Autonomous multi-agent conversations that solve problems too complex for a single AI model.

We build production systems with Microsoft AutoGen — multi-agent frameworks where specialized agents collaborate, debate, and self-correct to deliver reliable outputs.

Who this is for

Built for these teams and businesses

Research and analytics teamsSoftware development shopsComplex problem-solving use casesOrganizations that have pushed single-agent limits

Use cases

What we build for you

◆Autonomous code review and generation pipelines
◆Multi-agent research synthesis systems
◆Self-verifying analysis and QA workflows
◆Complex reasoning chains with specialist agents
◆Automated software testing and debugging

Problems we solve

— Single agents making unchecked errors
— Complex tasks requiring multiple specialist "perspectives"
— Need agents that can verify and critique each other
— Building AI workflows that require reasoning depth

Tech stack

Platforms and tools we use

Microsoft AutoGenOpenAI APIAnthropic Claude APIAzure OpenAICustom tooling

What you get

Deliverables

◆ Multi-agent architecture

◆ Agent role design

◆ Production deployment

◆ Observability and logging

Typical timeline: 3–10 weeks

Process

How an engagement works

Discovery call

We map your workflows, understand your tools, and identify the highest-ROI agent opportunity in your business.

Architecture design

We design the agent: inputs, outputs, integrations, error handling, and the logic that makes it reliable in production.

Build and integrate

We build the agent, connect it to your tools and data, and run it through real scenarios — not sanitized demos.

Test and harden

We stress-test edge cases, add monitoring, and make sure the agent degrades gracefully when something unexpected happens.

Deploy and hand off

We deploy to production, set up alerting, and train your team to manage and iterate on the agent going forward.

Why Lumeor

What to expect working with us

◆Production deployments only — we don't hand over prototypes and call them done
◆We set up monitoring and alerting on every agent we ship
◆Transparent about what AI can and can't do reliably
◆Model-agnostic — we pick the right tool for your use case, not what we're certified in
◆We document everything so your team can maintain and extend what we build
◆30-day stabilization period included in every engagement
◆We've shipped agents for businesses across every major US industry

FAQ

Common questions

How long does it take to build and deploy an AI agent?

Most simple agents (single workflow, clear inputs/outputs) are live in 1–2 weeks. More complex agents with multiple integrations or multi-agent architectures take 3–8 weeks. We'll give you an accurate timeline after our first call.

What happens if the agent breaks after you deliver it?

We include a 30-day stabilization period in every engagement. If something breaks, we fix it. We also set up monitoring and alerting so we catch issues before you do. Post-stabilization support retainers are also available.

Can you build agents that connect to my existing software?

Yes — that's the norm, not the exception. We integrate with CRMs, ERPs, ticketing systems, communication tools, databases, and custom APIs. If it has an API or webhook, we can connect an agent to it.

Which AI models and platforms do you use?

We're model-agnostic and platform-agnostic. We use OpenAI, Anthropic Claude, Google Gemini, and open-source models depending on the use case. For automation, we commonly use n8n, CrewAI, LangChain, and custom Python builds.

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