The multi-agent AI development company that gets agents working as one system

A single agent is one reasoning loop with a set of tools working toward a goal. A multi-agent pipeline is the orchestration layer above that: several specialised agents — often a planner, workers and a critic — each doing part of the job and handing off to the next. The extra power comes with extra failure modes, mainly errors compounding across agents and cost multiplying, which is exactly what the orchestration, tracing and review stages are there to contain. We build the single-agent layer too; this is the coordination on top of it.

The frameworks are good and we use them — LangGraph, CrewAI and AutoGen all have their place. What they don't give you is the part that makes a pipeline production-worthy: typed handoff contracts, shared state done properly, tracing that spans the whole run, end-to-end evaluation and cost control. A demo pipeline is a day's work; one you can trust with real requests is the actual project, and that is where we spend the time.

Three ways, together. Typed handoff contracts so a bad output is caught at the boundary rather than silently accepted; critic and validation stages that can reject and return work instead of only passing it on; and end-to-end evaluation that scores the final outcome, so a regression anywhere in the chain shows up. The aim is to catch a mistake at the stage it happened, not three agents downstream where it's already been laundered into something plausible.

Build cost tracks the number of agents, the tools each one needs and the orchestration between them. Running cost is the thing to watch: every agent makes its own model calls, so a single request can fan out into many inferences, which we budget and instrument during the build before you commit to it. On timeline, a scored end-to-end prototype is usually a few weeks; a traced, cost-controlled production pipeline is typically a few months, depending on how many agents and systems are involved.

No. Shared state stays within your environment and accounts wherever the architecture allows, each agent runs with scoped access rather than a shared key, and your data isn't used for anything outside your project. Where a hosted model provider sits in the design we tell you which one, what leaves your infrastructure and what their retention and training terms are before you approve it.

Whenever a simpler design would do, which is often. If the steps are fixed, a deterministic pipeline is cheaper and more reliable. If one agent can hold the whole task, the extra agents just add cost, latency and failure modes. And if the task is latency-sensitive, fanning it out across several sequential model calls may be a non-starter. We would rather talk you out of an unnecessary pipeline than bill you to run one.

It's designed for, not hoped against. Each stage has a timeout and a defined failure path, so a stalled or looping agent degrades gracefully instead of hanging the request, and partial work is handled rather than left dangling. Pipeline-wide tracing shows exactly which agent and which handoff failed, the failure becomes a regression scenario, and there's a kill switch for taking the pipeline down cleanly if you need to.

Our own in-house engineers in Sahibzada Ajit Singh Nagar, Punjab — 45+ of them, with over a decade of combined delivery experience, delivering for clients in 15+ countries. Nothing is subcontracted. You meet the engineers who will be on your project before you sign, and the person demonstrating the work each sprint is the person who built it.