AI Engineering Service

AI Agent Development Services for Enterprise Automation

We design and deploy production-grade AI agents that reason, plan, and execute complex tasks. From single-agent workflows to multi-agent orchestration, we build systems that operate your business processes intelligently.

Why AI Agents

What AI Agents Enable for Your Business

AI agents go beyond chatbots. They reason over problems, select tools, take actions, and handle complex multi-step workflows without constant human input.

Autonomous Execution

Agents reason, plan, and execute multi-step tasks without manual intervention for each step.

Multi-Agent Teams

Specialised agents collaborate — a researcher, analyst, and writer — completing complex workflows together.

Tool and API Use

Agents call APIs, query databases, read documents, and write outputs as part of real business processes.

Persistent Memory

Stateful agents remember context across sessions and long-running workflows through LangGraph state graphs.

Our Services

AI Agent Development Services We Deliver

Autonomous Agents

Autonomous AI Agent Development

Build goal-driven agents that reason, plan, and execute. Integrate tools, APIs, and memory to create agents that complete real work without constant human input.

CrewAI

Multi-Agent Orchestration with CrewAI

Design collaborative AI teams where specialised agents — researcher, analyst, writer — work together as a crew to complete complex business workflows.

LangGraph

LangGraph Stateful Agent Workflows

Build production-grade stateful pipelines with persistent memory, human-in-the-loop controls, branching logic, and reliable rollback for long-running processes.

RAG

RAG and Agentic Search Systems

Combine retrieval-augmented generation with agentic reasoning for document Q&A, knowledge bases, and semantic search grounded in your private data.

AutoGen

AutoGen Conversational Agents

Deploy multi-agent conversational systems where agents collaborate, debate, and verify outputs — ideal for code generation, analysis, and research tasks.

Strategy

AI Agent Architecture Consulting

Expert guidance on framework selection, agent design patterns, integration strategy, observability, and production readiness for your specific use case.

Framework Comparison

Choosing the Right AI Agent Framework

Not all agent frameworks are the same. We assess your requirements and recommend the most appropriate architecture.

Framework Best For State Management Multi-Agent Use Case Examples
LangGraph Complex stateful workflows with branching and cycles Built-in persistent state graphs Via supervisor patterns Document processing, long-running approvals, HR workflows
CrewAI Role-based collaborative agent teams Context sharing between agents Native crew collaboration Research automation, content pipelines, sales intelligence
AutoGen Conversational, debate-style verification Conversation history Native dialogue-based Code review, fact checking, quality assurance
LangChain Agents Single-agent tool use and RAG integration Memory modules Limited, via LangGraph Customer support bots, document Q&A, search assistants
Use Cases

AI Agent Use Cases by Business Function

Customer Support Agent

Handles queries, retrieves account data, escalates edge cases, and resolves tickets end-to-end across chat, email, and WhatsApp.

HR and Onboarding Assistant

Answers policy questions, processes onboarding documents, and guides employees through procedures autonomously.

Sales Intelligence Agent

Researches prospects, scores leads, drafts personalised outreach, and updates the CRM without manual input.

IT Helpdesk Agent

Diagnoses issues, runs resolution playbooks, resets credentials, and logs tickets with full context automatically.

Finance and Procurement Agent

Validates invoices, routes approvals, extracts contract terms, and flags anomalies in financial workflows.

Knowledge and Research Agent

Searches and synthesises information from internal documents, external sources, and databases on demand.

Tech Stack

Our AI Agent Technology Stack

Agent Frameworks

LangGraph, CrewAI, AutoGen, LangChain, Agno

LLM Providers

OpenAI GPT-4o, Anthropic Claude, AWS Bedrock, Google Gemini

Vector Databases

Pinecone, pgvector, Weaviate, OpenSearch, FAISS

Deployment

AWS Lambda, ECS, Kubernetes, LangSmith observability

FAQs

Frequently Asked Questions

What is the difference between LangGraph, CrewAI, and AutoGen?

LangGraph is best for stateful, branching workflows with persistent memory. CrewAI excels at role-based multi-agent collaboration. AutoGen is designed for conversational, debate-style agent verification. We assess your use case and recommend the right framework.

How long does it take to build an AI agent?

A focused single-agent workflow can be production-ready in 4–6 weeks. Complex multi-agent systems typically take 8–16 weeks depending on integrations, memory design, and tooling scope.

Can AI agents integrate with our existing systems?

Yes. Agents connect to CRM, ERP, HRMS, databases, REST APIs, email platforms, and custom applications through secure, testable tool-use patterns.

How do you ensure agents are reliable in production?

We build human-in-the-loop controls, tool-call validation, error recovery pipelines, cost management, and full observability. LangGraph state persistence ensures reliable recovery from failures.

Ready to Build Your AI Agent?

We help organisations move from manual, repetitive workflows to intelligent, autonomous operations. Schedule a free consultation to explore what AI agents can do for your business.

Schedule Discovery Call