[ Systems that act, with the brakes built in first ]
An agent is a model given tools and a goal. That is powerful and it is dangerous, and the difference between the two is entirely in the limits. We build the permissions, the approvals and the audit trail in the first sprint — not after the first incident.
An agent is a model given tools and a goal. That is powerful and it is dangerous, and the difference between the two is entirely in the limits. We build the permissions, the approvals and the audit trail in the first sprint — not after the first incident.
Tell us the workflow and where it must stopAn agent gets scoped credentials for exactly the actions it needs. Read-only until a specific write has been argued for and approved.
Payments, contracts, outbound messages to customers and record deletions pause for a person. The agent prepares; a human commits.
What it was asked, what it retrieved, which tool it called with which arguments, and what came back. When something goes wrong you need the trace, not a guess.
Hard caps on steps, time and spend per task. An agent that retries forever is not a bug you find in testing; it is one you find on the invoice.
Asked
What is the refund window on a bulk order?
Retrieved from your documents
Answered
Bulk orders over ₹50,000 can be returned
within 21 days of delivery, against the
standard 7 days. The goods must be unopened
and in original packaging.
The model arranged the sentence. Every fact in it — the amount, the window, the condition — came from a document you own, and the source is attached so a wrong answer can be traced rather than argued about.
Multi-step internal work: reconcile a statement, chase a document, triage an inbox, prepare a report. The agent gathers and drafts; a person approves the commit.
Given a list, the agent goes and finds what is missing — company details, contacts, specifications — with the source for each field so it can be verified.
The part most agent projects skip: permissions, approvals, tracing, spend caps and a kill switch, built so the agent can be trusted with more over time.
Exactly what the agent may do, what it must never do, and what "done" looks like. Most failed agent projects skipped this and discovered the boundaries in production.
Each action is an ordinary, tested API with validation and its own permissions. The agent is a caller of good tools, not a replacement for building them.
Step caps, spend caps, timeouts, an approval gate on consequential actions, and a kill switch that works while the agent is mid-task.
Against a copy of your data with real tasks, including the ones designed to trip it. We are looking for the failure modes here, not the happy path.
One workflow, one team, approvals on everything. The gate widens on evidence — measured completion and error rates, not confidence.
Traces, cost per task, completion rate and human-intervention rate on a dashboard. An agent that quietly gets worse is the normal failure, not a dramatic one.
Agents are the part of AI where the failure modes get expensive. A chatbot that gets something wrong writes a bad sentence. An agent that gets something wrong sends the wrong invoice, emails the wrong customer or deletes the wrong record — and does it forty times before anyone notices.
So we build in an unusual order: the tools and the limits first, the autonomy last. The agent starts as a very good assistant that prepares work for approval, and earns each removal of a gate with measured evidence. That is slower to demo and considerably cheaper to own.
Every action is a plain, validated, tested API. An agent wired to sloppy tools is a faster way to make the same mistakes.
Approval gates come off one at a time, on evidence from the traces. Nothing goes fully automatic because the pilot went well for a fortnight.
When an agent does something surprising, the only useful thing is a complete record of what it saw and what it called. We build that first.
Structured function calling now works consistently across the major models, which moved agents from a research demo to something you can put behind a queue.
The interesting question is no longer whether the model can plan the steps. It is which credentials it holds, what it may commit without asking, and who is accountable when it does.
Queues, retries, idempotency and state machines — the same things any batch system needs. Teams that treated an agent as a chat loop learned this the hard way.
An agent that reads a web page or an email can be instructed by it. Untrusted content is quarantined from the instruction path, and consequential tools stay behind approval.
A chatbot answers. An agent acts — it can call your systems, take several steps, and change something. That makes it far more useful and far riskier, which is why the engineering effort goes into permissions, approval gates and an audit trail rather than into the conversation.
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