SERVICE · AI AGENT DEVELOPMENT

LangGraph agent development, with human-in-the-loop handoff built in

We build AI agents that can be trusted in front of real customers: LangGraph state machines with validated tool calls, a clean handoff to a human when the agent is out of its depth, and the monitoring to know when that happens.

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WHY THIS EXISTS

The part most teams underestimate.

An agent demo takes an afternoon. An agent that runs unattended for thousands of customers takes the work nobody puts in the demo: what happens when a tool call has the wrong argument type, when an external API times out mid-conversation, when the customer asks for something the agent shouldn't attempt, or when the conversation state gets corrupted halfway through a flow.

Our founder's day job has been exactly that work: building the AI agent and omnichannel support infrastructure for a national highway operator with over 500,000 customers. That includes the monitoring, fallbacks, human handoff and self-healing state that let an agent run in production without someone watching every conversation. This service applies the same patterns to your agent.

WHO IT'S FOR

Built for teams like yours.

  1. 01

    Support and operations teams

    You want an agent to resolve a real share of customer conversations across chat, WhatsApp or email, and hand the rest to your team with full context.

  2. 02

    Products adding agentic workflows

    Your users need multi-step tasks done for them, like looking things up, filling forms or calling your APIs, with approval steps where they matter.

  3. 03

    Teams with an agent that misbehaves

    You have a LangChain or LangGraph agent in production that loops, drops context, calls tools wrongly or fails silently, and you need it stabilized.

PROBLEMS WE SOLVE

What goes wrong, and how we handle it.

  1. 01

    Tool calls with bad arguments

    Models don't always respect tool schemas. Gemini, for example, sometimes sends numbers where a schema expects strings. We validate and coerce every tool argument with Pydantic before execution, so a type mismatch becomes a fix rather than a crash.

  2. 02

    No safe exit

    Every graph gets explicit handoff paths: confidence thresholds, out-of-scope detection and customer requests for a human. These route the conversation to a person with a summary and the full state attached.

  3. 03

    Silent failures

    Structured logging and tracing on every node and tool call, with alerts on fallback rates, handoff spikes and latency, so problems show up on a dashboard before they show up in complaints.

  4. 04

    Fragile state

    Checkpointed graph state with recovery paths, so an interrupted conversation resumes correctly instead of restarting or contradicting itself.

DELIVERABLES

What you get.

  1. 01

    Agent design document

    The graph: nodes, tools, state schema, handoff conditions and every failure path, agreed before code.

  2. 02

    LangGraph implementation

    The agent in Python or TypeScript, with typed state and validated tool calls.

  3. 03

    Tool layer

    Integrations with your APIs and data, each with input validation, timeouts and retries.

  4. 04

    Human-in-the-loop handoff

    Escalation into your existing helpdesk or inbox with a conversation summary and state.

  5. 05

    Monitoring

    Traces, logs and alerts for fallbacks, handoffs, errors and cost, wired into your cloud's logging.

  6. 06

    Regression scenarios

    Scripted conversations that cover the critical flows, rerun before every prompt or model change.

TIMELINE

A realistic schedule.

  1. 01

    MapWeek 1

    Walk through real conversations and tasks, define what the agent owns and what it always hands off, and design the graph.

  2. 02

    PilotWeeks 2–4

    Build the graph and tools against staging systems and run the regression scenarios until the core flows pass.

  3. 03

    Shadow and limited releaseWeeks 4–6

    Run on a slice of real traffic with aggressive handoff thresholds, review transcripts and tune.

  4. 04

    ScaleWeek 6 onward

    Relax thresholds as evidence accumulates, and keep monitoring and the regression suite in place.

Engagement shape

  • Pilot scoped and priced as a fixed project. Ongoing tuning available as a monthly retainer if you want it.
  • We integrate with your existing channels and helpdesk rather than replacing them.
  • Releases are gated on the regression scenarios passing, not on a demo looking good.

Stack

  • LangGraph
  • LangChain
  • Python
  • FastAPI
  • TypeScript
  • Pydantic
  • Gemini
  • Claude
  • OpenAI
  • PostgreSQL
  • Redis
  • GCP
  • OpenTelemetry
PROOF

Where we've done this before.

  • Not an agent, but the same discipline: multi-step generation, schema validation, quality checks and a fallback on every call.

  • Structured, schema-bound model output feeding a real-world document a customer acts on.

QUESTIONS

AI Agent Development FAQs.

  • Customer-facing agents need explicit state, branching, retries and interrupt points for human approval. LangGraph models the conversation as a graph with checkpointed state, which makes those paths visible, testable and recoverable. For a single-step task we'll tell you that you don't need an agent at all.

  • The graph has defined exits: the customer asks for a person, the agent's confidence drops below a threshold, or the request falls outside its scope. At that point the conversation is routed to your team with a summary and the full state, so the human doesn't start from zero. The agent can also pause for approval before sensitive actions.

  • Yes. The agent sits behind a channel layer, so the same graph serves multiple channels. Our founder's production work involved exactly this kind of omnichannel setup, routing customer conversations from several channels into one agent and one human team.

  • Every node and tool call is traced and logged. We track handoff rate, fallback rate, tool errors, latency and cost per conversation, alert on spikes, and review transcripts during rollout. The regression scenarios catch breakage before release.

  • Whichever passes your scenarios at the lowest cost and latency. We've shipped agents on Gemini and work with Claude and OpenAI models too. The tool and state layers are model-agnostic, so switching providers is a configuration change plus a regression run.

  • Yes. Our founder has shipped Gemini Live streaming speech-to-text and WebRTC voice in production, including reconnect handling and mute-aware streaming. Voice adds latency and turn-taking constraints, so we scope it as its own phase.

OTHER SERVICES

Often paired with.

  • AI Product Development

    LLM features that survive real users: structured outputs, RAG, guardrails and cost control on Gemini, Claude or OpenAI.

  • SaaS MVP Development

    2–6 week Next.js + Supabase + Stripe MVPs with auth, billing, the core workflow and launch-ready SEO.

  • Web Development

    Next.js marketing sites and web apps with fast Core Web Vitals and technical SEO built in, not bolted on.

Talk to us about ai agent development.

Share the problem, the people who have it, and where you are today. We'll reply with questions and an honest read on scope and fit.

SSttaarrtt  aa  pprroojjeecctt