Sarvaswa AI Labs
AI Agent Development

AI agents that do the work, rather than describe it.

We design and build custom AI agents that reason over your data, call the tools your team already uses, and carry multi-step work through to completion inside your own infrastructure. The difference shows up in the smallest examples: when a customer asks to change a billing address, a chatbot can explain where the setting lives, while an agent opens the record, makes the change, writes the audit entry, and tells you exactly what it did. Everything we build is gated by evaluations you can inspect and handed over as code your team owns.

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The distinction that matters

Most AI projects stall at the point where something has to actually happen.

When a chatbot receives a question it produces text in return, which is genuinely valuable in a help centre where the explanation is the deliverable, and considerably less valuable everywhere else, because the moment a person needs a real change inside a real system, a human being still has to go and make that change by hand. The work did not move forward during that conversation. It was described accurately, and then it sat exactly where it was.

An AI agent differs in one specific and consequential way: it holds a set of tools and it decides for itself which of them to call in which order. That means it can query your warehouse, read the relevant contract, open a ticket in your tracker, post an update to the right channel, and then check its own result against a test before it reports anything back to a person. When a step fails, which happens more often than demonstrations suggest, a well-built agent notices the failure and recovers rather than confidently continuing with a broken assumption.

This is the reason we build agents instead of chat wrappers, and it is also the reason agents are meaningfully harder to build well. As soon as software is permitted to act on your systems, questions that were previously theoretical become operational: which records may it write to, what happens when it is wrong, who approved the action, and how would anyone reconstruct the sequence afterwards. Most of the engineering described further down this page exists to answer exactly those questions, because the model itself was never the hard part.

What we build

Five agent patterns we ship most often.

Almost every engagement begins from one of the five shapes below and is then specialised to your domain, the tools you already run, and the level of risk your team is willing to carry. Reading them in order is useful, because they get progressively more capable and progressively more demanding to operate safely.

Single-task agents

A single-task agent does one job reliably, thousands of times over, using a deliberately narrow set of tools, a clearly defined stop condition, and an objective check on whatever it produces. We recommend that most teams start here, not because the pattern is simple, but because a narrow agent is straightforward to evaluate and therefore straightforward to trust, and trust is the thing that determines whether your team ever lets the second agent run.

  • Reading an inbound support ticket, classifying it by root cause, routing it to the right queue, and drafting a first reply for a human to approve
  • Extracting the line items from a supplier invoice, matching them against the purchase order, and posting the result to the ledger for approval

Multi-agent systems

Some problems genuinely require several kinds of expertise at once, and in those cases a supervisor agent breaks the incoming task into discrete pieces, delegates each piece to a specialist child agent, and then synthesises everything it receives back into one coherent answer that a person can audit. Each child agent carries its own tools and its own permissions, which matters because the agent reading policy documents has no business holding write access to your payments system.

  • An insurance claim where one agent checks policy terms, another cross-references fraud patterns, and a third calculates reserves before a supervisor assembles the recommendation
  • A regulatory filing where data extraction, policy interpretation, and validation run in parallel and are reconciled into a single reviewable document

Tool-using and MCP agents

This is the integration layer that allows an agent to act on your systems at all, and in practice it is where a large share of the engineering effort lands. We build Model Context Protocol servers and custom tool interfaces that run inside your own perimeter and are scoped to the least privilege that still lets the work happen, so the agent can reach your real data without any of that data leaving the boundary your security team already approved.

  • MCP connectors into your repository, issue tracker, data warehouse, and messaging platform, each scoped separately
  • A read-only analyst agent that can query production data freely but has no path to write to it, which makes it safe to hand to contractors

Conversational and voice agents

Sometimes a conversation really is the correct interface, particularly when the person on the other end is a customer who will not learn your internal tooling, and in those situations we build customer-facing assistants and voice agents that have genuine tool access behind the conversation. The distinction from an ordinary chatbot is that the exchange ends in a completed action rather than a polite promise that somebody will follow up later.

  • An AI receptionist that answers the call in natural conversation, books the appointment, and updates the practice CRM before the caller hangs up
  • A support assistant that performs the account change it is discussing, then confirms the change with a reference the customer can quote

Autonomous background agents

These agents run on a schedule or in response to an event rather than waiting for somebody to type a prompt, and because nobody is watching while they work, they ship with hard limits on tokens, iterations, and wall-clock time. This is the discipline we call loop engineering: the agent finds the work on its own, completes it, verifies the result mechanically against something that can genuinely fail it, and escalates anything it could not resolve with the evidence attached.

  • An overnight run that triages the previous day's failures and leaves a drafted fix with passing tests waiting for review each morning
  • Continuous monitoring that investigates anomalies as they appear and posts a summary with the supporting data to your team channel

The production gap

Six things that separate a demo from a system.

Building an agent demonstration takes an afternoon, and this is precisely why so many teams are surprised by what the real thing costs. An agent your colleagues are willing to leave running unattended requires the six disciplines below, and honestly, this list is where most of an engagement's effort and most of its value actually sit.

Evaluation harnesses

An evaluation harness is a test suite for behaviour rather than for code, built from the real cases your team has already handled, and graded so that you can demonstrate an agent genuinely improved after a change instead of simply hoping that it did. Without one, every adjustment to a prompt is an educated guess, and the team ends up relitigating the same disagreement about whether last week's version was better.

Scoped permissions

Every agent we build starts read-only, and each write permission it later receives has to be justified, scoped to the narrowest surface that still allows the work to complete, and re-audited on a schedule rather than granted once and forgotten. An agent that can take action is, by definition, an additional attack surface inside your environment, and we would rather build as though that were true than discover it later.

Human approval gates

Nothing irreversible happens without a person signing off on it, which in practice means the agent drafts, proposes, and stages the change while a human being performs the final approval. Where the stakes are genuinely low the gate can move or disappear, but that is always a deliberate decision made with your team rather than a default we quietly ship.

Context engineering

This covers the retrieval pipelines, prompt registries, and memory design that put the right information in front of the agent at the right step of its work. It deserves the attention because the overwhelming majority of agent failures turn out to be context failures rather than model failures, which is encouraging news: problems of that kind are solved with engineering you control, not by waiting for a larger model to be released.

Observability and tracing

Every run is traced from beginning to end, recording which tools the agent called, the arguments it passed to them, and the reasoning that led it to choose those tools in that order. The value of this becomes obvious the first time an agent does something unexpected at three in the morning, because at that moment what your on-call engineer needs is a complete transcript, not a summary and an apology.

Cost and latency control

We select models on a per-step basis rather than defaulting to the largest available one, add caching where the same context is fetched repeatedly, and set hard budget caps that stop a runaway loop before it becomes an invoice. An agent that performs its task capably while costing more than the human work it replaced is a failed project regardless of how well it demonstrates, so we instrument spending from the first week and model it before you commit.

How we build

Narrow first, and then widen.

Agent projects tend to fail when they begin broadly, because a system that touches eight workflows at once gives you no way to tell which part is working. We therefore start with the single workflow that has a measurable outcome and an objective check, prove the pattern there, and extend the agent's reach only once your team has watched it behave well.

  1. Scope, over one to two weeks

    We map the candidate workflows with the people who currently perform them and select the one with the clearest success signal, which is usually not the one with the most enthusiasm behind it. You receive an architecture, a projection covering both build and running costs, and an honest assessment of which parts of the process should not be automated at all.

  2. Build the narrow agent

    One workflow, real tools, real data, running in your environment rather than a sandbox that flatters the result. The evaluation harness is built alongside the agent instead of being added afterwards, for the straightforward reason that the harness is the thing that tells us, and you, whether the agent is ready to be trusted.

  3. Gate and instrument

    Permissions are scoped, approval gates are placed where the stakes require them, and tracing and cost monitoring are wired in. This is the phase that converts a working prototype into something your team can leave running while they get on with other work, and skipping it is the most common reason promising agent pilots never reach production.

  4. Widen, then hand over

    We add more tools, more workflows, or additional agents under a supervisor, but only at the pace your evaluation results justify. At handover your team receives the source code, the skill files, the evaluation harness, and the runbook they will operate it with, and most engagements reach production somewhere between eight and fourteen weeks from kickoff.

Scope, honestly

Agents for work you can verify, and humans for judgment.

We keep agents well away from irreversible financial actions, clinical and legal judgments, and any workflow where being correct is a matter of professional opinion rather than something a check can settle. This is not because the models are incapable of producing a confident answer in those situations, but because nobody can reliably tell you afterwards whether the answer they produced was actually right, and an unverifiable answer delivered at scale is a liability rather than an asset.

If your workflow has no automated way to reject bad output, we will say that an agent is the wrong tool before you spend money finding out for yourself. In a reasonable number of conversations the honest recommendation turns out to be a better-designed form, a fixed integration between two systems that never spoke properly, or a well-aimed prompt inside a tool your team already pays for. We would much rather give you that answer in the first week than invoice you for a system that quietly costs more than it returns.

What you keep

The agents, the tools, the evals. All of it is yours.

Everything runs inside your cloud and within your data boundaries, with SOC 2, HIPAA, and any region-specific requirements scoped at the beginning rather than discovered during a security review. At handover you receive the source code, the MCP servers and tool integrations, the evaluation harness, the prompt registry, and the runbook your team will operate the system with, which together are everything required to run and extend it without us.

We stay tech-agnostic by design, selecting Anthropic Claude, open models such as Llama or Mistral, or a combination, according to the latency, cost, and compliance constraints you actually have, and never according to a stack we are paid to resell. The practical consequence is that you are not renting an agent from us on an ongoing basis. You own a system your engineers can read, modify, and extend, and that ownership is what turns it into a durable advantage rather than a subscription.

Related: Chatbot development applies this pattern to customer-facing conversations, Loop Engineering covers agents that run unsupervised on a schedule, Claude Enablement covers the Skills and MCP servers agents run on, and hiring AI developers is the option when you would rather build the agent with your own team.

Questions worth answering

AI agent development FAQ.

A chatbot receives a question and returns text, whereas an AI agent holds a set of tools and decides which of them to call, which means it can query a database, read a document, open a ticket, run code, and verify its own output before it reports anything back. The practical difference is easiest to see in an example: asked about an overdue invoice, a chatbot explains your payment terms, while an agent looks up the specific invoice, checks whether a reminder was already sent, drafts the follow-up, and leaves it for approval. That additional capability is also why agents need engineering a chatbot never requires, including scoped permissions, evaluation harnesses, human approval gates, and complete tracing of every run.
An engagement covers the architecture and model selection, the agent itself, the tool and MCP integration layer that connects it to your systems, an evaluation harness built from the real cases your team has already handled, scoped permissions and approval gates, observability and cost instrumentation, and the runbook your team operates it with afterwards. We build five patterns most often, which are single-task agents, multi-agent systems coordinated by a supervisor, tool-using and MCP agents, conversational and voice agents, and autonomous background agents that run on a schedule without anybody watching.
Scoping runs for one to two weeks and produces an architecture, a cost projection covering both the build and the running spend, and an honest assessment of what should not be automated at all. Most engagements then reach production between eight and fourteen weeks from kickoff. We deliberately begin with one narrow workflow that has an objective success check, prove that it works, and widen the agent's reach only once your team trusts the pattern, because agent projects that begin broadly are consistently the ones that stall.
There are two costs that matter and we model both before you commit to anything. The build cost is scoped after the one to two week discovery, once we know which workflow we are automating and which systems it has to reach. The running cost depends on how models are selected at each step, how much context each call carries, and how often the agent retries, so we instrument token spend from the first week and choose models per step rather than defaulting to the largest one available. An agent that performs its task well while costing more than the work it replaced is a failed project, which is why we would rather show you that in the projection than after your first invoice.
A multi-agent system earns its complexity when a single problem genuinely requires several kinds of expertise, or several tool sets that should carry different permissions from one another. Claims triage is a good example, because checking policy terms, cross-referencing fraud patterns, and calculating reserves are three different jobs that a supervisor can run in parallel and then reconcile. When a single agent with a clear tool set can complete the work, we build that instead, since every additional agent adds capability and also adds a new way for the system to fail.
The protection comes from structure rather than from trusting the model to behave. Permissions begin read-only, and every write permission is scoped to the narrowest surface that still allows the work to complete and re-audited on a schedule. Nothing irreversible happens without a human approval gate in front of it. An evaluation harness built from your real cases has to pass before any change ships, and every run is traced end to end so that you can see which tools were called and why. Beyond all of that, we simply keep agents out of workflows where nothing exists that could objectively reject a bad answer.
We remain tech-agnostic and select per workload rather than by habit. Anthropic Claude is our default for most production agents because of how it handles tool use and long context, while open models such as Llama or Mistral come in where cost, latency, or an on-premise requirement makes them the better fit, and a single system often uses more than one. On the integration side we build Model Context Protocol servers that run inside your perimeter. In every case the choice follows the constraints you actually have, and never a stack we are paid to resell.
Yes, entirely. The source code, the MCP servers and tool integrations, the evaluation harness, the prompt registry, and the runbook all belong to you at handover, and the system runs inside your own cloud and data boundaries throughout. There is no Sarvaswa-hosted layer that you continue to depend on afterwards, which means your engineers can read, modify, and extend the work without involving us. The competitive advantage that the system creates belongs to you rather than to your vendor.

Tell us the workflow, and we will tell you whether an agent fits.

Fifteen minutes is usually enough to test your use case against the three things that determine whether an agent works: a clear success signal, real tools for it to call, and something that can objectively reject a bad answer. If an agent turns out to be the wrong tool for the job, we will tell you that, and the assessment costs you nothing.

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