Offering · AI Agent Workflows

Production AI agents — with the engineering behind them.

Agentic workflows with human-in-the-loop review, escalation logic, tool integrations via MCP, and observability built in from day one. Agents that don't just demo well — they ship and keep running.

Who It's For

For agents being built, shipped, or stabilized.

New build

You have a workflow in mind.

A specific business process — customer support, claims triage, sales ops, data extraction — that needs an agent doing real work, not answering FAQs.

Stuck

The marketplace agent doesn't fit.

You bought an off-the-shelf agent, or wired one up with a no-code tool, and it can't integrate with your systems, your CRM, your data.

In production

Shipped — and misbehaving.

It works most of the time. The rest of the time costs are spiking, behavior is drifting, and you can't tell why. No observability, no guardrails, no eval.

Deliverables

What you get.

A production AI agent with the engineering required to run it for real.

01

Agent Architecture

Framework selection, orchestration patterns, state management. Built for the workflow you actually have — not a generic chatbot scaffold.

02

Tool Integration via MCP

Connect the agent to real systems — CRMs, databases, internal APIs, your knowledge base — using Model Context Protocol. Typed, audited, testable.

03

Human-in-the-Loop Review

Approval gates, escalation logic, confidence thresholds, human override paths. Humans see what matters; the agent runs the rest.

04

Guardrails & Structured Output

Schema-validated outputs, refusal patterns, rate limits, content filters. Deterministic where it has to be.

05

Observability & Cost Control

Langfuse traces, per-call cost attribution, latency dashboards, retry/timeout policies. You see what the agent is doing — and what it's costing.

06

Evaluation Harness

Golden dataset, automated evals, regression checks. Catch behavior drift before users do — and gate deploys on quality.

How It's Built

The agent harness — battle-tested, not aspirational.

We've shipped agents into production for our own products and clients. The harness is the part most teams underestimate: orchestration, state, retries, fallbacks, evals. That's where the work lives.

  • LangChain, LangGraph, or custom — picked for your case.
  • MCP for tool integrations — typed, audited, swappable.
  • Langfuse observability from day one.
  • Deployed to your cloud — you own the code.
Engagement at a Glance
Team2–3 senior engineers
Timeline~6 weeks (scoped)
ModelFixed scope
DeployYour cloud · your stack
HandoverCode · runbook · evals · training
PricingScoped — contact us

Agents that ship and keep shipping.

Tell us the workflow. We'll come back with a scoped plan — architecture, tool integrations, HITL points, eval strategy, and timeline.