LLM Development & AI Agents
Autonomous AI agents that do real work inside your enterprise.
We design and build production AI agents — from customer-support copilots and internal knowledge assistants to fully autonomous multi-agent systems that orchestrate complex business workflows. Our agent stack covers orchestration (LangGraph, CrewAI, AutoGen), the Model Context Protocol (MCP), retrieval, long-term memory and enterprise-grade guardrails.
Capabilities
What our llm development & ai agents practice delivers
Custom AI Agents
Purpose-built agents for support, sales, HR, legal, healthcare and finance workflows.
Autonomous & Multi-Agent Systems
Planner–executor architectures, agent swarms and supervisor patterns for complex processes.
Enterprise Chatbots
Brand-safe conversational AI across web, WhatsApp, Slack and Teams with human hand-off.
Voice Agents
Low-latency speech-to-speech agents for contact centers, scheduling and IVR replacement.
Internal Knowledge Assistants
Secure copilots over your wikis, drives, tickets and policies with per-user permissions.
Customer Support AI
Ticket triage, draft responses, resolution automation and CSAT-aware escalation.
Sales & Marketing AI
Lead qualification, research agents, proposal generation and CRM hygiene automation.
HR, Legal & Compliance AI
Policy Q&A, contract review, onboarding assistants and compliance monitoring.
Agent Orchestration
LangGraph, CrewAI and AutoGen pipelines with retries, state, tracing and evaluation.
Model Context Protocol (MCP)
MCP servers and clients that safely expose your internal tools and data to AI agents.
RAG & Memory
Hybrid retrieval, episodic and semantic memory so agents stay grounded and context-aware.
Guardrails & Evaluation
Input/output filtering, policy enforcement, red-teaming and continuous evaluation harnesses.
Why it matters
Benefits you can measure
Real automation, not demos
Agents that complete tickets, draft contracts and reconcile data — measured by resolved work.
Safe by design
Permission-scoped tools, audit logs and approval gates for every consequential action.
Model-agnostic
Claude, GPT, Gemini and open models behind one abstraction — switch without rewrites.
Observable & testable
Full tracing, replay and regression evaluation for every agent decision.
Engagement
How the engagement runs
Map the workflow
Identify the human process, tools, data and decision points the agent must handle.
Design the agent
Choose orchestration pattern, tool contracts, memory and guardrail policy.
Build & evaluate
Iterate against golden datasets and shadow-mode runs before any autonomy.
Deploy & supervise
Progressive autonomy with human approval gates, tracing and cost dashboards.
Industries we serve with this practice
Technologies & platforms
FAQ
Common questions
A chatbot answers questions. An agent plans multi-step work, calls tools and APIs, maintains state and completes tasks — like resolving a support ticket end-to-end or reconciling invoices — with humans approving consequential actions.
Ready to talk llm development & ai agents?
Get a free consultation with a senior architect from this practice — scoped recommendations within one week.