Move beyond chatbots. Design, build and deploy AI agents that plan, call tools, remember context and coordinate as multi-agent teams — using Claude, LangGraph, MCP and the real engineering stack hiring teams expect.
Agentic AI is the next layer on top of GenAI. Instead of a single LLM reply, an agentic system breaks a goal into steps, picks tools, queries memory, calls APIs, evaluates its own output and decides the next move. Algo Labs teaches this as a software-engineering discipline — not a prompt trick — so you ship agents that survive production.
This page is a focused entry point. The end-to-end build experience lives inside Mastering GenAI & Agentic Systems and the 8-month GenAI Career Track, where agentic AI is taught alongside RAG, deployment and capstone projects.
This page focuses on agentic concepts. The full hands-on curriculum is delivered inside the flagship Mastering GenAI & Agentic Systems program and the 8-month GenAI Career Track.
No. You need comfort with Python and basic APIs. We don't assume PhD-level ML — we focus on the engineering patterns that make AI agents actually work.
All three, with LangGraph as the primary deep dive. Each framework solves a different orchestration problem, and we explain when to reach for which.
Yes. MCP is treated as a first-class topic — how to expose tools safely, scope permissions, and integrate MCP servers into a working Claude agent.
Deployable. Every project ships to a hosted endpoint (Streamlit / FastAPI / Azure) so it lives in your portfolio as a real running URL.
Live online cohort sessions with mentor support. Recordings are shared for revision, but the learning happens live.
Pick what you need and we'll open WhatsApp with your message ready to send. Replies during working hours.
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Live online training from Thiruvananthapuram, Kerala. Open to learners across India.