We build production-grade AI agent systems — from single-purpose task agents to multi-agent orchestration frameworks — grounded in your enterprise data and governed for real-world use.
Agentic AI is software that plans, uses tools, and completes multi-step tasks autonomously — unlike generative AI, which only produces content in response to a prompt. As an agentic AI development company, BootLabs builds production-grade, RAG-grounded agents on frameworks like LangGraph and CrewAI, governed to ISO 27001 standards for BFSI and enterprises across India and the UAE.
See how this applies in financial services, our AI engineering and AI-in-SDLC practice, and real case studies.
Agentic AI refers to AI systems that can autonomously plan, decide, use tools, and take multi-step actions to accomplish goals — not just respond to prompts. An agent doesn't just answer a question; it looks things up, reasons across sources, drafts a response, checks it against rules, and acts.
The difference between a chatbot and an agent is the difference between a search box and a colleague. Agents work. They complete tasks, handle exceptions, escalate when needed, and get better over time.
Single-purpose agents that handle specific enterprise tasks end-to-end — answering complex queries across systems, processing documents, generating drafts, checking compliance, and handling routine decisions autonomously.
Complex workflows that require multiple agents working together — a research agent, a reasoning agent, a writing agent, a validation agent — coordinated by an orchestration layer that manages state, routing, and escalation.
Agents that go beyond text — they call your APIs, query databases, trigger workflows, create tickets, update records, and interact with enterprise systems in controlled, auditable ways. Built with strict permission boundaries and human approval gates.
The platform layer that makes agents reliable in production — model routing, fallback chains, prompt versioning, cost tracking, latency monitoring, safety filters, and continuous evaluation against golden test sets.
These aren't POCs. These are production deployments, grounded in real enterprise data, solving real operational problems.
Agentic AI only works in the enterprise if it's grounded in your actual data and governed from the start. We build upward from your systems of record — with governance at every layer.
Ensuring responsible AI deployment with human oversight, transparent audit trails, and safety gates at every action boundary.
The only layer where GenAI components reside — combining LLMs with enterprise-specific business logic, domain rules, and multi-step reasoning across sources.
Normalising and enriching fragmented enterprise data — structured records, documents, SOPs, contracts, logs — into a unified knowledge base the agents can reason over.
The authoritative sources. Agents never own the truth — they read from these systems. We integrate, never replace.
We don't start with a technology pitch. We start by understanding what decision or task costs your team the most time — and design backward from there.
We map the use case, define the data sources, identify integration points, and design the agent's decision logic — including where human-in-the-loop approval is required.
We build the agent, connect it to your data sources, and test it exhaustively against real enterprise scenarios — including adversarial inputs, edge cases, and hallucination probes.
We deploy with full LLMOps infrastructure — cost tracking, latency monitoring, prompt versioning, safety filters — and run it through ROC for 24/7 operational oversight.
Every card below is a live deployment. Real data, real workflows, measurable outcomes.
Generative AI produces content — text, code, images — in response to a prompt. Agentic AI goes further: it plans, uses tools, calls APIs, and takes multi-step actions to complete a goal, with a human in the loop where it matters. In enterprise settings, generative AI drafts an answer; an agent looks up your data, reasons across sources, acts, and checks the result against your rules.
We're framework-agnostic and choose per use case, working with LangGraph, CrewAI, the OpenAI and Anthropic SDKs, and LlamaIndex or LangChain for retrieval. Agents are RAG-grounded in your systems (PLM, ERP, DMS, CRM) and can run on managed or on-premise models where data residency demands it.
Yes. Every agent ships with governance built in — full audit trails, role-based access, confidence scoring, safety gates, and model observability — and is engineered to ISO 27001 standards, which makes it suitable for regulated BFSI and enterprise use. See our AI engineering practice for how we take models to production.
A focused discovery maps 2–3 high-value use cases, and we target a production-ready agent in about 90 days. Cost depends on scope, integrations, and model choice — scoped precisely in the discovery call. Explore representative outcomes in our case studies.
Talk to our agent engineering team. We'll map 2–3 use cases that can go to production in 90 days.