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enquiry:+91-9121642626AI agent development services: agentic AI that plans, acts and gets work done
Tvisha is an agentic AI development company building AI agents that take a goal, work out the steps, use your tools and systems to complete them, and hand off to a person when it matters - with the guardrails that make that safe to run in production.
What is agentic AI?
Most AI tools wait for a prompt and return an answer. An agentic AI system is given a goal instead: it breaks the goal into steps, decides which tool or system to use for each one, acts, checks the result, and adjusts. The difference is the move from AI that talks about work to AI that does part of it.
Our AI agent development covers the whole arc: choosing the right workflow, designing the agent and its tools, building in guardrails and human approval, and monitoring it in production - delivered by the same in-house team from first estimate to long-term support. For the difference between the two terms, see our blog on agentic AI vs AI agents.
Plan · Act · Remember · Stay in bounds · Hand off to people
Tell us what you're looking for and we'll get back to you.
Our team is a call away in both India and the USA.
Agentic AI development services, from one agent to a coordinated team
Seven service lines, one in-house team. Whether you are automating a single workflow or coordinating several agents across a business process, the same engineers carry it from design through production.
Custom AI Agents for Business
Agents built around one job and done properly: a defined goal, the tools it may use, the data it can see, and clear limits on what it may decide alone. Built with frameworks such as LangChain and LangGraph in Python or Node.js, and scoped to your process rather than adapted from a generic template.
Multi-Agent System Orchestration
When one agent is not enough, specialist agents share the work - one researches, one drafts, one checks - coordinated by an orchestrator that routes tasks, resolves hand-offs, and keeps the whole run traceable.
Tool Use & System Integration
An agent is only as useful as what it can reach. We connect agents to your CRM, databases, and internal APIs through well-defined tool interfaces, including MCP servers, with permissions scoped to the task.
Knowledge, Memory & Retrieval
Agents that answer from your documents and remember what matters across a task. We ground them with retrieval (RAG) over your own content, using pgvector on PostgreSQL or Supabase, and, where it is warranted, custom LLM development for domain-specific behaviour.
Agentic Workflow Automation
End-to-end automation where the steps are not fixed in advance: triaging requests, gathering information from several systems, taking the next action, and escalating exceptions - beyond what a rules-based script can handle.
Guardrails & Human-in-the-Loop Controls
Autonomy you can trust: least-privilege access, approval steps for consequential actions, spending and action limits, and a full audit log. People stay in charge of the decisions that matter.
Agent Evaluation, Monitoring & Optimization
Agents behave differently on real data than in a demo. We build test sets from your real cases, measure task success and failure modes, and monitor live runs so quality and cost stay under control after launch.
Where agentic AI earns its place
Agents suit work that is multi-step, spread across systems, and too variable for a fixed script - these are the patterns we see most.
Customer Support Resolution
Agents that look up the account, act on the request, and escalate only what needs a person.
Recruitment & HR Screening
Shortlisting, scheduling, and first-round screening handled before a recruiter steps in.
Research & Reporting
Gathering, cross-checking, and summarising information from many sources into a usable brief.
Back-Office Operations
Document handling, data entry, and reconciliation across systems that do not talk to each other.
Sales & Order Workflows
Qualifying leads, updating the CRM, and moving orders through the steps to fulfilment.
Engineering & IT Workflows
Triage, routine diagnostics, and first-pass fixes that free engineers for harder problems.
When you need an agent, and when a chatbot is enough
Agents cost more to design and govern than a chatbot, so we will tell you honestly when the simpler tool is the right one - our AI chatbot development service covers that side.
- The job is answering questions from a known body of content
- A person will take every action that follows the answer
- One turn or a short conversation completes the task
- The task takes many steps across more than one system
- The next step depends on what the previous one returned
- You want the work completed, not just advised on
How an agentic AI engagement runs
Autonomy is earned in stages - each one checked before the next begins.
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01
Discover & Scope
Pick one bounded workflow, map its steps and systems, and agree what success looks like.
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02
Design & Build
Agent logic, tools, and memory built in short iterations you can review as it grows.
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03
Evaluate & Harden
Tested on your real cases, with guardrails and approval steps switched on from day one.
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04
Deploy & Improve
Monitored release, then wider autonomy only as the agent proves itself in production.
The AI agent development stack we use
Model-agnostic by design: agents are built with LangChain and LangGraph, in Python or Node.js, with the agent logic kept separate from the model, so you are not locked to one provider.
Claude
GPT
Gemini
Llama
Mistral
LangChain
LangGraph
pgvector
Supabase
Python
Node.js
TypeScript
Docker
AWS
Azure
Google Cloud
Why teams trust Tvisha with their agentic AI work
An agent that works in a demo is easy. One you can leave running is a delivery problem.
Controlled autonomy
Approval steps, limits, and audit logs are designed in from the start, not bolted on later.
Built into your systems
Agents work through your existing software, so nothing your team relies on has to be replaced.
Enterprise delivery discipline
20+ years and 500+ projects of software delivery experience sit behind every engagement.
A dedicated in-house team
100+ developers across two offices; the same engineers stay on your project throughout.
An agentic AI development company you can verify
Numbers you can check, not just claim.
Agentic AI & AI agent development FAQs
The ones that come up on nearly every first call about agentic AI.
What is the difference between agentic AI and a chatbot?
A chatbot answers questions inside a conversation. An agentic AI system is given a goal, breaks it into steps, uses tools and business systems to carry those steps out, and checks its own progress. It acts, rather than only replying.
Is it safe to let an AI agent take actions in our systems?
It can be, when the agent is designed for it. We give agents least-privilege access, define which actions need human approval, log every step, and add limits on what an agent may do on its own. Autonomy is widened only as the agent earns trust.
Which AI models do you build agents on?
We are model-agnostic and choose per task: hosted models such as Claude, GPT and Gemini, or open models such as Llama and Mistral when data must stay in your environment. The agent logic is kept separate from the model so it can be swapped later.
Can an agent work with our existing software?
Yes. Agents connect to your existing systems through APIs and tool interfaces, including MCP servers, so you do not need to replace the software your team already uses.
Where should we start with agentic AI?
With one well-bounded workflow that is repetitive, rule-heavy and easy to measure. We scope it in a short discovery, build it with human review switched on, and expand only once it performs reliably.
More AI services for business
Ready to hire an AI agent development company?
Tell us which workflow eats your team's time and we'll scope a first agent around it - no generic estimate.
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