Dapr Agents - Fix Outdated Agent Answers
Duration: 30 minutes
Supported languages: Python. Requires an OpenAI API key and a free Tavily API key.
Large language models are frozen at training time, so they confidently return answers that may already be out of date. In this hands-on track you'll build an end-user-facing expert agent with Dapr Agents that closes that gap by reaching out to the live web. The agent calls a Tavily-backed search tool to pull in current information, then reasons over those results with an LLM — so the answers it gives users stay grounded in today's data instead of stale training knowledge.
In this self-paced course, you'll learn:
- Why LLMs return stale or outdated answers, and how grounding responses in live web results solves it.
- How to build an end-user-facing expert agent with Dapr Agents in Python.
- How to add a web search tool backed by the Tavily API to fetch current information.
- How to connect the agent to an LLM through the provider-agnostic Dapr Conversation API.
- How to combine tool calls with agent reasoning so answers stay accurate and up to date.
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