Ground Agents in Live Data
Stop agents guessing at facts that change. Give them a small set of well-described tools backed by live APIs, return errors they can act on, and keep every conversation inside a credit and latency budget.
Ground Agents in Live Data badgeComplete all 4 lessons to earn it. About 2.3 hours.
0 of 4 lessonsStart course
What you'll be able to do
- Decide which questions an agent must answer with a tool instead of from memory
- Design a small, well-named tool set and narrow wrappers instead of exposing raw APIs
- Return tool errors the model can act on, and stop an agent loop on fatal ones
- Cache, parallelise and budget tool calls so each conversation has a known cost
- Measure tokens, credits and latency per turn with real numbers
This course is one of three skill areas on exam APV-220, APIVerve Certified Developer: AI Agents: 10 of its 30 questions. See the full path
Syllabus
- Why agents need live dataStart hereWhy language models hallucinate times, rates and lookups, which questions need a tool call, and how to ground an agent in live API data in Node and Python.
- Choose and scope toolsDesign tools for AI agents that get picked correctly: fewer tools, clear names and descriptions, strict schemas, and narrow wrappers over raw APIs.
- Handle tool errors in agentsHandle tool call errors in an AI agent loop: return errors the model can act on, treat 401, 403 and 429 differently, retry safely and never loop forever.
- Control cost and latencyControl AI agent cost and latency: count credits per tool call, cache tool results, run tool calls in parallel, cap each conversation and measure every turn.