AI agent development that survives production
Grounded retrieval, constrained tool use, evaluation, and an audit trail in one system.
2muchcoffee is an AI agent development company building 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.
Talk to experts nowRead the work before you hire us
Read the code- Multi-agent pipeline, in codeSix agents on LangGraph with retrieval and a self-reflection loop that re-prompts its own writer.
- An agent service, in codeRead app/ai_match: the prompts, the tools, the event stream, and how the agent is wired into a real API.
- The same agent, runningOur own product, deployed. Open it and use the matching agent yourself.
- Production RAG teardownSee how we approach grounding, retrieval, and evaluation.
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.
How we work
A short, honest process, the same whether you bring us in as a dedicated team, an outsourced build, or a hybrid of both.
Scope
A short call to pin down what the agent needs to do, what it can touch, and where a human stays in the loop. We would rather cut scope than over-build.
Ground it
Most useful agents stand on retrieval. We build the grounding first, over your data, so the agent reasons from something real.
Build and constrain
The orchestration, the tools, and the guardrails, with the limits and the audit trail built in rather than bolted on afterwards.
Evaluate
Golden sets and retrieval-precision checks, so we can show it works and catch it the moment it stops.
Deploy and watch
Into production as a real service, with the observability to see what it actually does once real users touch it.
Where agents earn their keep
Not every task needs an agent. The ones that do are the messy, multi-step jobs a single prompt cannot hold together: reading from several systems, deciding what to do next, and acting on it. These are the ones we build for.
Customer-facing support
Agents that answer from your own documentation and data, escalate when they are unsure, and stay inside the actions you allow. Grounded, so the answer is yours, not the model's guess.
Back-office automation
The repetitive operations that quietly eat your team's week: triage, data entry across systems, routing, reconciliation. The agent handles the path, a human approves what matters.
Research and analysis
Agents that gather from many sources, compare, and summarize with citations, so the output is checkable rather than a confident paragraph you have to take on trust.
Document and data processing
Pulling structured data out of documents, email, and PDFs at volume, with the validation that stops a wrong field from flowing downstream into everything else.
Why agents break in production, and how we stop it
A demo agent and a production agent are different animals. The demo works because the path was happy and the data was clean. Production is neither. These are the failure modes we engineer against from the start, not after the first incident.
It makes things up
An ungrounded agent fills gaps with plausible fiction. We constrain answers to retrieved, cited context, so when it does not know, it says so instead of inventing.
It does the wrong thing
An agent with open-ended access will eventually take an action you did not intend. We give it a constrained set of tools, human checkpoints on anything costly or irreversible, and an audit trail of every step.
It quietly gets worse
A prompt tweak or a model update can regress an agent with nobody noticing until users do. An eval harness with golden sets catches the regression before release, not after.
It runs away on cost or latency
Loops, retries, and oversized context turn into a bill and a timeout. We build in budgets, timeouts, and provider-agnostic routing, so you choose cost against capability per task.
Stack
An agent without an eval loop is a liability with a friendly tone. We build the measurement and the guardrails in.
Orchestration
- LangGraph
- Python
Retrieval
- Qdrant
- pgvector
- Hybrid search and reranking
Models
- OpenAI
- Anthropic
- Provider-agnostic routing
Safety and ops
- Tool use with guardrails
- Eval harness (golden sets, retrieval precision)
- Observability
Want a cost and timeline range first? Get an estimate
What agents has the 2muchcoffee 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 long does an AI agent take to build?
It moves with how many systems it touches and how tight the compliance bar is. A scoped pilot is usually weeks, not months. We start with the smallest version that proves the value, then expand from there.
What does AI agent development cost?
The range moves with scope, integrations, and the compliance bar. You can get a fast, honest range from our estimator, and every real number is scoped on a call rather than pulled from air.
Does our data or code stay private?
Yes. Grounding runs on your data under your constraints, and a provider-agnostic model layer means we can route or self-host where a requirement calls for it. We review your data handling before a line of code ships.
Can you work with the stack we already have?
Yes. We build inside your codebase and your systems rather than asking you to start over. The agent plugs into what you already run.
How do we start an agent project?
A short call to scope what the agent needs to do and what it can touch, then we agree the model and timeline.
Related reading
Recent write-ups from our engineers.
Have an agent to build?
Tell us what it needs to do, what it can touch, and where a human stays in control
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