Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
LangGraph and Agent Patterns: A Practical Primer
- Graphs versus linear chains: when and why to use them.
- Agents, tools, and planner-executor loops.
- Hello workflow: creating a minimal agentic graph.
State, Memory, and Context Passing
- Designing graph state and node interfaces.
- Differentiating between short-term memory and persisted memory.
- Managing context windows, summarization, and rehydration.
Branching Logic and Control Flow
- Conditional routing and multi-path decision-making.
- Implementing retries, timeouts, and circuit breakers.
- Handling fallbacks, dead-ends, and recovery nodes.
Tool Use and External Integrations
- Executing function/tool calls from nodes and agents.
- Consuming REST APIs and databases from within the graph.
- Parsing and validating structured outputs.
Retrieval-Augmented Agent Workflows
- Strategies for document ingestion and chunking.
- Utilizing embeddings and vector stores with ChromaDB.
- Generating grounded responses with citations and safeguards.
Evaluation, Debugging, and Observability
- Tracing paths and inspecting node interactions.
- Using golden sets, evaluations, and regression tests.
- Monitoring quality, safety, and cost/latency.
Packaging and Delivery
- FastAPI serving and dependency management.
- Versioning graphs and implementing rollback strategies.
- Developing operational playbooks and incident response protocols.
Summary and Next Steps
Requirements
- Working knowledge of Python.
- Experience in building LLM applications or prompt chains.
- Familiarity with REST APIs and JSON.
Audience
- AI engineers.
- Product managers.
- Developers building interactive LLM-driven systems.
14 Hours
