Building Agents
for Enterprises
A production-focused, 8-week cohort for AI/ML engineers who already ship code and want the systems layer of agentic AI — routing, state, persistence, and failure handling — not another prompting primer.
Built for engineers who already ship
This isn't an intro to LLMs. Check yourself against the spec before you apply.
You write production code and are comfortable in Python — APIs, error handling, and deploys aren't new to you.
You've called an LLM API directly (not just used a chat app) and understand tokens, context, and tool calling at a basic level.
You want the systems layer of agents — state, control flow, retries, persistence — for a real enterprise use case.
You're new to programming, or this would be your first hands-on work with an LLM API.
Eight weeks, four phases
Every phase builds directly on a LangGraph project you're already running — nothing here is a standalone exercise.
Foundations of the agent graph
- The graph mental model: nodes, edges, and shared state
- Tool-calling and ReAct-style agents in LangGraph
- Structured, schema-constrained model output
Control flow that survives production
- Conditional routing and multi-agent handoff
- Persistence and checkpoints across runs
- Time travel: replaying and rolling back agent state
Making it enterprise-grade
- Fault tolerance and retry strategies for flaky tools
- Human-in-the-loop approval gates
- Tracing and observability across multi-step runs
Ship it
- Deploying a LangGraph service behind an API
- Latency, cost, and scale trade-offs
- Capstone: a production-shaped enterprise agent, reviewed live
Six systems, not six demos
Multi-agent router
Conditional handoff between specialized agents based on live state.
Tool-calling ReAct agent
An agent that reasons, calls real tools, and acts on the result.
Checkpointed graph
State that survives restarts, with recoverable execution.
Time-travel debug session
Replay a run, branch from any step, and fix it without starting over.
Fault-tolerant retry pipeline
Graceful handling of flaky tools and partial failures.
Human-in-the-loop gate
An approval checkpoint before an agent takes a high-stakes action.
The specifics
Sessions are recorded, but this is built as a live, cohort experience — the projects, feedback, and pace assume you're showing up each week, not watching later.
Prudhvi
AI/ML educator and solution architect who has run cohort-based coding and ML courses for several years, with hands-on experience building RAG pipelines and production AI systems across healthcare, eCommerce, and retail.
Before you apply
Do I need LangGraph experience already?
No. You should be comfortable with Python and have called an LLM API directly at least once. The graph framework itself is taught from Phase 01.
Is this live or self-paced?
Live weekly sessions with async project work in between. Recordings are available, but the cohort is built around showing up live.
What will I actually walk away with?
A capstone-shaped, production-style enterprise agent — plus the six systems built across the eight weeks: routing, tool-calling, checkpointing, time travel, retries, and human-in-the-loop gates.
Can my company cover the cost?
Yes — reach out after applying and we'll get you what you need for an expense or PO.