Your teams know ChatGPT. We enable them to build autonomous, production-grade enterprise AI agents that actually run your business.
Most GenAI pilots are RAG wrappers or Q&A bots. Real enterprise value comes from agents that plan, use tools, collaborate, and operate autonomously at scale.
Faster AI delivery when teams use reusable agent patterns vs building from scratch each time
Teams ready to move beyond prototypes into production agent systems
The delta between a working ChatGPT wrapper and a production multi-agent system is enormous — and most teams are stuck at the chatbot stage.
Most internal "AI agents" are Q&A or RAG-based — they can't plan, can't use tools, and can't act. They answer; they don't do.
Existing GenAI pilots lack orchestration, memory, and tool integration. Every demo is a dead end when it hits a real workflow.
No framework to govern autonomous AI systems at scale. Who controls the agent? What happens when it fails? Compliance is an afterthought.
AI knowledge stays with one or two people. Skills don't spread across the org — creating a single point of failure and a bottleneck on every AI initiative.
From agent architecture to production governance — everything your team needs to design, build, and own autonomous AI systems.
Design multi-agent systems with LangGraph and AutoGen. Move from single-prompt chains to stateful, graph-based agent orchestration.
Connect agents to APIs, databases, and enterprise data sources. Give your agents real knowledge and real capability.
Build collaborative agent workflows using CrewAI. Design systems where specialized agents work together on complex, multi-step enterprise tasks.
Apply enterprise guardrails, observability, and compliance. Build agents that can be trusted, monitored, and controlled.
| # | Module / Topic | Key Coverage | Duration |
|---|---|---|---|
| 1.1 | Evolution: RPA to Agentic AI | Rule-based automation → ML models → LLMs → Agents. Key limitations of each era. | 1.5 hr |
| 1.2 | Core Concepts of AI Agents | Perception, reasoning, memory, action, tool-use. Difference between chatbot, assistant, and agent. | 1 hr |
| 1.3 | Agent Design Patterns | ReAct, Plan-and-Execute, Reflexion, Multi-agent. When to use each. | 1 hr |
| 2.1 | OpenAI Agents SDK Architecture | Installing the SDK, understanding Agents, Runners, and Threads. | 1 hr |
| 2.2 | Tools, Function Calling & Handoffs | Defining tools, structured outputs, agent-to-agent handoffs. | 1.5 hr |
| 2.3 | Memory & Context Management | Thread state, conversation history, long-context strategies. | 1 hr |
| 2.4 | Guardrails, Tracing & Observability | Input/output guardrails, built-in tracing, logging agent runs. | 1 hr |
| 3.1 | LangChain Fundamentals | Chains, prompts, retrievers, memory. How LangChain's model compares to imperative LLM sequences. | 1 hr |
| 3.2 | LangGraph — Stateful Agent Graphs | Nodes, edges, state machines. Why graph-based orchestration solves linear chain limitations. | 1.5 hr |
| 3.3 | RAG with LangChain | Vector stores, document loaders, retrieval-augmented generation pipelines. | 1.5 hr |
| 3.4 | Tool Ecosystem & LangSmith | Community tools/integrations, LangSmith for evaluation and debugging. | 1 hr |
| 4.1 | Framework Comparison | OpenAI SDK vs LangChain vs AutoGen vs CrewAI — side-by-side feature matrix. | 1 hr |
| 4.2 | Choosing the Right Stack | Decision framework: task complexity, team maturity, latency, cost. | 0.5 hr |
| 5.1–5.4 | Enterprise Use Case: Customer Support Agent | Architecture walkthrough, implementation with OpenAI SDK + LangGraph, production readiness, evaluation. | 5 hrs |
| 6.1 | MCP & Tool Standardization | Model Context Protocol — interoperability and early adoption patterns. | 1 hr |
| 6.2 | Multi-Agent & Collaborative Systems | Supervisor-worker patterns, AutoGen, CrewAI. | 1 hr |
| 6.3 | Q&A, Roadmap & Next Steps | Open floor, recommended learning paths, enterprise readiness checklist. | 0.5 hr |
This is a build workshop, not a lecture series. Participants leave with working code, reusable patterns, and the confidence to own agent systems in production.
Real agent pipelines built during the workshop — not toy demos. Deployable into enterprise workflows.
Teams that can architect, ship, and govern autonomous AI agents — not just use the ones someone else built.
A library of patterns applicable across customer support, operations, finance, and any business function.
Faster AI delivery with guardrails baked in from day one — not retrofitted after a compliance incident.
Developers, Tech Leads, and Architects ready to build production agent systems
On-site bootcamp · Live virtual · Hybrid cohort
Book a free 45-minute "Is Your Engineering Team AI Ready?" session. We'll map your team's current AI readiness and give you a clear action plan — at no cost.
Book Free CTO Session →Let's talk about your team's current stack, your target use case, and how we design the right cohort for your organization.