In this session, we’ll explore how to leverage Postman AI Agent Builder and Postman Flows to construct multi-stage AI agents that:

Embed and Index Data Use Postman’s integration with vector databases (e.g., Pinecone) to generate embeddings for large document collections, enabling fast, semantic similarity searches.

Perform Semantic Retrieval Execute real-time vector searches within Flows to fetch the most relevant content chunks based on user queries, eliminating manual lookups.

Drive Decision Logic with LLMs Chain AI request blocks to feed retrieved context into LLMs (OpenAI GPT, Claude, etc.), letting the model produce answers, summaries, or routing decisions as next-stage actions.

Orchestrate Multi-Stage Workflows Visually compose branches and loops in the drag-and-drop Flow canvas, integrating conditional logic, error handling, and parallel steps—all without writing glue code.

Test and Iterate Rapidly Leverage Postman’s built-in testing and mock servers to simulate agent responses, validate logic against edge cases, and fine-tune prompts in a collaborative environment.

Deploy and Monitor Export your Flow as a runnable collection or integrate with CI/CD, then monitor agent performance and usage via Postman’s analytics and logs.

Attendees will leave with:

A clear understanding of how vector search augments LLMs in agent contexts. Ready-to-use Postman Flow templates combining embeddings, search, and AI blocks. Best practices for modularizing agent stages and handling errors gracefully. Strategies for collaborative API-first AI development and rapid prototyping