AI Agent Development Company | Agents in Production | 2muchcoffee

AI agent development

2015Shipping software since
10M+Users on our builds
5.0Clutch, 26 reviews

We build AI agents that do real work, not chatbots that fall over the moment a task gets complicated. Retrieval-grounded, evaluated, with guardrails, and deployed as real services. You can read our public agent repositories before you decide.

What we build

Agents that are dependable because they are well-engineered underneath, not because the demo happened to go well.
  • Multi-agent systems
    Agents that coordinate on a task, with the orchestration and state handling that keep them predictable instead of chaotic.
  • Tool-using agents
    Agents that act on your systems through constrained tools, with audit trails so you can see exactly what they did.
  • RAG-grounded agents
    Agents that answer from your data, standing on retrieval we built and wrote up in public.
  • Guardrails and evaluation
    The limits, grounding, and eval loop that keep an agent safe in production and catch regressions before your users do.

Have an agent to build? Tell us what it needs to do and what it can touch, and we will scope it.

Talk To Experts Now

The proof
Read the code before you hire

Our engineers maintain public multi-agent repositories, so you can read the actual code rather than take a claim on faith. The proof is code and writeups, not slides.
The retrieval layer most useful agents stand on is one we wrote up in full, our hybrid RAG on Qdrant teardown, including the grounding and evaluation work. Behind it is a company shipping production software since 2015, with a 5.0 rating on Clutch across 26 reviews.

Stack

An agent without an eval loop is a liability with a friendly tone. We build the measurement and the guardrails in.

LangGraphPythonQdrantpgvectorHybrid search and rerankingOpenAIAnthropicProvider-agnostic routingTool use with guardrailsEval harness (golden sets, retrieval precision)Observability

Questions

What agents has your team built?

We maintain public multi-agent repositories you can read, and we published a full teardown of the retrieval layer most agents depend on. The proof is code and writeups, not slides.

Agents or RAG, which do I need?

Usually both. Retrieval is the layer under most useful agents, so we build them together: grounded retrieval first, then the agent that reasons over it.

How do you keep an agent from doing something wrong?

Guardrails, constrained tool use, grounding, and evaluation. The agent acts within limits you set, on cited context, and an eval harness catches regressions before release.

Which models and frameworks do you use?

LangGraph for orchestration, with OpenAI and Anthropic models behind a provider-agnostic layer so you can route by cost, capability, or a self-hosting requirement.

How do we start?

A short call to scope what the agent needs to do and what it can touch, then we agree the model and timeline.

CONTACT OUR TEAM

Do you have an idea for your next project? Not sure what tech stack or business model to choose? Share your thoughts and our team will assist you in any inquiry.
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