From use-case alignment to production agents - RAG, tools, orchestration, and continuous optimization.
AI Agentic Practice
AI Agentic Solutions
Gensten AI Agentic Solutions help enterprises move beyond chatbots to production-grade agents: goal-driven systems that retrieve knowledge (RAG), call enterprise tools via MCP, orchestrate multi-agent workflows, and stay governed under an Agent Development Lifecycle (ADLC). We combine LLM engineering, retrieval layers, Microsoft Azure AI Foundry, Fabric IQ, and OneLake so agents are accurate, auditable, and scalable.
Goal-driven agents with tool use and enterprise system integration
Multi-agent orchestration with human-in-the-loop controls
RAG-grounded answers on private data (OneLake, vector stores)
ADLC: plan → build → test → deploy → operate → monitor
Microsoft Foundry IQ & Fabric IQ ready architectures
Architecture Layers
How Agentic AI fits into a production agentic stack.
1
Experience Layer
Chat, copilots, APIs, and embedded agent UIs for employees and customers.
2
Agent Orchestration Layer
Planning, routing, multi-agent collaboration, and policy enforcement.
3
Reasoning Layer (LLM)
Foundation and fine-tuned models for planning, synthesis, and tool selection.
4
Retrieval Layer (RAG)
Chunking, embeddings, vector search, reranking, and citation grounding.
5
Data & Knowledge Layer
OneLake, Fabric IQ, enterprise DBs, documents, and governed catalogs.
6
Integration & Tool Layer
MCP servers, APIs, ERP/CRM, ticketing, and secure action execution.
7
Observability & Governance
OpenTelemetry traces, evals, audit logs, cost controls, and retirement paths.
What makes an AI solution “agentic”?
Agentic systems don’t just answer questions - they decompose goals, choose tools, retrieve evidence, act in enterprise systems, and loop until KPIs are met. That requires structured planning, retrieval quality, safe tool permissions, and runtime monitoring - not a single prompt.
Plan → act → observe loops with clear stop conditions
Tool calling with least-privilege scopes
Memory and context windows managed via RAG and session state
Evaluation harnesses before and after release
Enterprise-ready agent platforms
We implement agent stacks on Azure AI Foundry, open-source frameworks, and hybrid models - with Fabric IQ and OneLake as the governed knowledge backbone when you standardize on Microsoft’s data estate.
Use Cases
IT service desk agents with ticket triage and resolution
Knowledge workers copilots grounded on SharePoint / OneLake
BFSI compliance assistants with audit trails
Supply-chain exception agents with ERP tool actions
Sales research agents with CRM write-back
Technologies & Platforms
Azure AI FoundryMicrosoft Fabric / OneLakeOpenAI / GPTLangGraph / Semantic KernelMCPOpenTelemetryVector DBs (Azure AI Search, Pinecone, Weaviate)
Frequently Asked Questions
How is agentic AI different from a chatbot?
Chatbots mainly respond to prompts. Agentic systems plan multi-step work, call tools/APIs, retrieve private knowledge via RAG, and can complete business workflows under governance.
Do you support multi-agent architectures?
Yes. We design specialist agents (research, coding, ops, compliance) coordinated by an orchestrator with shared memory, policies, and evaluation metrics.
What business problems are best suited for AI agents?
High-volume, multi-step work with clear KPIs: service desk triage, knowledge Q&A with actions, compliance checks, exception handling in ops/finance, and research workflows that write back to CRM or ERP.
How do you keep agents from taking unsafe actions?
Least-privilege tool scopes, human-in-the-loop approval for sensitive actions, policy checks before tool calls, audit logging, and staged promotion through ADLC Test & Release gates.
What is MCP and why does it matter for enterprise agents?
Model Context Protocol (MCP) standardizes how agents connect to tools and data sources. It helps enterprises integrate systems consistently with clearer auth, observability, and reusable connectors.
Can agents use our private company data securely?
Yes. We ground agents with enterprise RAG over governed stores (including OneLake), enforce ACLs at retrieval time, and keep sensitive data out of prompts unless authorized.
How do you measure ROI for agentic solutions?
We define KPIs in the Plan stage - cycle time, deflection rate, accuracy, cost per task, and CSAT - then track them in Operate/Monitor with OpenTelemetry and business dashboards.
Do you build on Microsoft Azure AI Foundry?
Yes. Many clients standardize on Foundry IQ for models, agents, evals, and governance, with Fabric IQ and OneLake as the knowledge and semantics backbone.
What does a typical Gensten agentic engagement include?
Discovery and KPI design, architecture (layers + tools), RAG corpus build, agent development, evaluation suite, production deployment, and a hypercare Operate/Monitor period.
Can we start with one agent and scale later?
Absolutely. We recommend a single high-value agent with strong evals, then expand to multi-agent orchestration once retrieval quality, tooling, and governance are proven.
