VYAN LABS / AI AGENTS
Custom AI agents that do the work.
Give an agent a defined job, the right tools, and a clear boundary. We build systems that can act, ask for help, and show why they did what they did.
Start a project01 / WHAT WE BUILD
AI agents.
Useful agents need more than a prompt. They need context, reliable tool access, and a way to measure their decisions.
A defined job
Scope the user, task, context, permissions, and escalation path before implementation.
Working integrations
Connect the agent to knowledge, internal systems, and the actions it is allowed to take.
Evals and visibility
Instrument traces, failure modes, review loops, and the handover your team needs.
02 / APPLICATIONS
Where it earns its place.
A few patterns we can adapt to your actual workflow and systems.
Customer-facing agents
Answer requests with grounded context, perform permitted actions, and route edge cases to a person.
Internal operations
Research, classify, draft, and update records across the tools your team already uses.
Knowledge and retrieval
Build retrieval pipelines with source visibility and quality checks, so answers can be trusted and improved.
03 / METHOD
Designed to be operated.
We build for the people who will use, review, and maintain the system after launch.
Define the boundary
Agree on the job, data, permissions, outcomes, and failure modes.
Test the real flow
Build incrementally with representative cases and clear feedback.
Hand it over
Document decisions, instrument operations, and train the team.
04 / PRODUCTION
Production AI agents need a controlled operating model.
The agent is only one part of the system. Tool permissions, evaluation cases, escalation rules, and a reliable backend determine whether it can be trusted.
Tool boundaries
Give each agent only the actions its job requires. Authenticate every request, validate arguments, and keep sensitive operations behind explicit approval.
Evaluation before release
Collect representative tasks and known failure cases. Check task completion, unsupported claims, unsafe tool calls, and handoff quality before a change reaches users.
Runtime visibility
Trace prompts, tool calls, latency, cost, and outcomes without exposing sensitive data in logs. Use those traces to fix recurring failures.
Enterprise data access
For natural-language access to structured data, the MCP server should enforce policy independently of the model. Our Text-to-SQL MCP note explains the security boundary.
05 / QUESTIONS
Good questions, direct answers.
A few details we usually cover before a project begins.
What can an AI agent actually do?
A useful agent can read relevant context, use approved tools, make bounded decisions, and return a result or ask for a human decision.
How do you handle incorrect or unsafe responses?
We define boundaries, add validation and escalation, then test the agent on representative cases. Logs and evaluations make failures visible.
Can it run on our infrastructure?
Deployment depends on your data and security requirements. We can design around your environment and existing services.
What do we receive at handover?
The working system, integration and deployment notes, evaluation approach, operational documentation, and a walkthrough with your team.
07 / CONTACT
Tell us what needs to work.
A useful first conversation starts with the workflow, constraints, and what success would look like.
Let’s make it real.
Share a brief outline. Sourish will reply directly to discuss scope, feasibility, and a sensible first milestone.
support@vyanlabs.in ↗