Agentic AI Systems That Finish the Work
Most agent demos are a prompt and a hope. We build agents the way we build any production system: clear boundaries, measurable outcomes, evaluations before launch, and a cost model you can defend. We run our own agent runtime, Navratna, so we have hit the failure modes already.
- Built our own multi-agent runtime (Navratna)
- Evals and verification before anything ships
- Cloud models, open-weight models, or both
Why most agent projects stall
An agent that works nine times out of ten is a demo. In production the tenth run is the one that emails the wrong customer, loops on a tool call until the bill arrives, or confidently invents a number in a report. The model is rarely the problem. The missing pieces are scoping, tool design, state, evaluation and the decision about where a human must approve.
We start from the job the agent has to finish and work backwards: which steps are deterministic code, which need a model, what has to be verified, and what happens when a step fails. The result is usually fewer agents than you expected, doing narrower jobs, with much higher reliability.
What we build
Agent architecture
Single agents, supervisor graphs or multi-agent councils, chosen for the job. State, memory and retries designed up front, not bolted on.
Tools and MCP integration
Typed tools over your APIs, databases and internal systems, exposed through the Model Context Protocol where it helps, with permissions scoped per agent.
Evaluations and guardrails
Test sets built from your real cases, automated scoring, verification steps for anything that touches money or customers, and human approval gates where they belong.
Cost-aware model routing
Simple steps go to small, cheap models; hard reasoning goes to frontier models. Routing is measured, so you can see cost per completed task.
On-prem and open-weight LLMs
When data cannot leave your network, we run open-weight models on your hardware and build the same agent workflows on top.
Operations
Tracing, dashboards and alerting for every run, so you can answer 'what did the agent do and why' without guessing.
Where agents pay off
- Internal operations: triage, reconciliation, report generation and follow-ups that today eat analyst hours.
- Customer support that resolves, not deflects: agents with real tool access and clear hand-off to humans.
- Engineering: coding agents in isolated workspaces that open reviewed pull requests rather than pushing to main.
- Document-heavy workflows: contracts, compliance evidence, KYC and claims, with every extracted value traceable to its source.
- Geospatial analysis: agents that query spatial data and produce maps and summaries, our own specialty.
Proof, not slides
Navratna, our sovereign agent runtime, runs multi-agent deliberation, governed artifacts, isolated resumable coding workspaces and adaptive routing across model families. Building it taught us where agent systems break, and that knowledge goes into every client build.
For one startup, a single-model pipeline was expensive and unstable under load. We introduced multi-agent routing and memory boundaries, cutting wasteful high-cost model calls while improving reliability. Details are on our case studies page.
Frequently asked questions
Which framework do you use: LangGraph, CrewAI or something custom?
Whichever fits the job and your team's ability to maintain it. We have shipped on LangGraph, compared it in detail against CrewAI and our own Navratna runtime, and we will tell you plainly if a framework adds more complexity than it removes.
How do you stop agents from hallucinating?
You can't make a model never wrong, so we design so that wrong answers are caught: retrieval with citations, verification steps, schema-checked outputs, evaluation suites run before every release, and human approval for high-stakes actions.
Can the agents run on our own infrastructure?
Yes. We deploy open-weight models on-prem or in your private cloud, so prompts and data never leave your network. Many clients run a hybrid: private models for sensitive steps, frontier APIs for the rest.
How long does a first version take?
A narrow, well-scoped agent with evaluations usually reaches a production pilot in four to eight weeks. We agree the success metric at the start so 'done' is measurable.
What does it cost to run?
We model cost per completed task before building, then measure it in production. Routing simple steps to small models typically cuts the bill substantially compared with sending everything to the largest model.
Describe the job you want an agent to do
Five fields. An engineer reads it and replies within one working day (IST), usually with a first opinion, not a sales pitch.