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Supervised agents that take a task, call your systems through scoped tools and finish the job, handing anything uncertain to a person with the full context attached.
Agents that hold context across long-running tasks, call your internal APIs, and know when to escalate to a human. Not a chatbot with a system prompt — a supervised worker with tools, memory and an audit trail.
Each tool is a typed function with its own permissions and rate limits, so an agent can act on your systems without holding broad credentials.
A planner hands work to narrow specialists for lookup, drafting and checking, so each step stays small enough to test and replace on its own.
Refunds, outbound messages and record changes above a threshold wait for approval in Slack or Teams, with the evidence and proposed action laid out.
Long-running work keeps its state between steps and sessions, so an agent can pause for a reply on Tuesday and pick up correctly on Thursday.
Every prompt, tool call, input and output is stored against a trace id, so any decision the agent made can be replayed and explained later.
Actions are idempotent where possible and reversible where not, so a failed step retries cleanly instead of sending the same email twice.
Every agent we ship follows the same five steps, and nothing reaches production without passing an eval set your team signed off.
We sit with the people who do the work today and write down every decision, system and exception, including the ones nobody mentions in the first meeting.
How autonomous ai agents shows up across India, the Gulf and the US. Pick one to see what changes in the process.
Coordinators check carrier portals by hand, phone drivers for delays and update customers one WhatsApp message at a time.
An agent watches tracking events, contacts the carrier, drafts the customer update and asks a coordinator only when a delivery promise must change.
Code, documentation and the tests that prove it — yours outright — and the limits it runs inside, agreed before anything goes live.
Each stage ends in something you can hold — a document, a demo, a passing eval, a dashboard. Nothing carries over on trust alone.
A paid two-week audit of your processes, data and systems. We come back with a ranked list of what AI should touch — and what it should not.
Audit report and ranked backlog
Model selection, retrieval design, guardrails and the integration surface. You get a written architecture with a cost model attached to it.
Architecture doc with cost model
Two-week sprints to implement agents, integrate with your systems and run internal evals. You see working software early and often.
A working demo in your environment
We run your real use cases, measure accuracy, latency and cost, and pressure-test edge cases with your team before go-live.
Evaluation report with KPIs
We help you launch, monitor and continuously improve. You get playbooks, dashboards and regular reviews to scale safely.
Live dashboards and runbooks
Still deciding?
Thirty minutes with an engineer who builds autonomous ai agents. No deck, no discovery form.
Talk to an engineerA chatbot answers questions. An agent completes a task: it reads a ticket, looks up the order, issues the refund within limits and closes the loop. That means it needs tools, permissions, memory and an audit trail, which is where most of our engineering time goes, rather than on the conversation itself.
Most engagements combine two or three of these. Discovery is where we tell you which.
Answers from your own documents, with the source cited.
ExploreModels tuned and tested on your task, not a benchmark.
ExploreProcesses that run end to end, people only on exceptions.
ExploreGuardrails, tracing and access control around every model.
ExploreA costed plan of what to build, buy or leave alone.
ExploreBring one process you think an agent could run.
We'll tell you straight whether it's worth building — and what it would cost if it is.