Agentic AI Framework (Enterprise Multi-Agent Architecture)
Designed an Enterprise Agentic AI Framework enabling autonomous multi-agent workflows for business processes, decision-making, development automation, and knowledge retrieval.
Problem
Organizations were adopting AI in isolated pockets, with no unified framework for orchestrating autonomous agents securely and reliably across business and IT operations. This led to duplicated efforts, inconsistent governance, and low ROI on AI adoption.
Approach
1. Researched global benchmarks for autonomous agent architectures (OpenAI, Google, Microsoft, AutoGPT, LangChain). 2. Designed a complete enterprise-ready blueprint covering: - Agent hierarchy (Supervisor, Worker, Specialist Agents). - Knowledge sources and vector store integrations. Tooling and API orchestration model Security and governance layers Cost-control and metering Human-in-the-loop checkpoints Conducted internal workshops to validate business use cases. Built prototype agent chains for IT operations, SDLC automation, and knowledge retrieval.
Solution
Developed a full Agentic AI Framework with the following components: Multi-agent orchestration engine (planner, executor, evaluator) Secure tool invocation layer for interacting with systems (APIs, databases, internal apps) Context memory architecture combining vector stores, RAG, and short/long-term agent memory Reusable agent templates: Architect Agent Developer Agent FinOps Agent Operations Agent Governance Agent Cost governance module defining credits, quotas, and budget controls Monitoring dashboard to track agent performance and usage This framework became the foundation for AI-enabled automation initiatives across the enterprise.
Impact
Enabled 70% faster automation of repetitive workflows across IT and business teams. Supported end-to-end SDLC automation, reducing development cycles by up to 40%. Introduced a governed model for safe, secure, and cost-efficient AI adoption. Accelerated AI experimentation and prototyping across departments. Positioned the organization as a leader in internal AI adoption with a scalable, structured model.