What is Tool Result Parsing in AI Agents?

An AI agent that calls a web search tool gets back a blob of JSON containing titles, URLs, snippets, and metadata. An agent that queries a database might get back hundreds of rows. An agent that calls an HTTP API might get back XML, a nested JSON object, or an error code. Tool result parsing is the bridge between what the tool returns and what the agent needs to proceed. At the simplest level, parsing means extracting the relevant fields from a structured response and presenting them to the model in a clean, token-efficient format. At a more complex level, it means handling errors gracefully — detecting when a tool returned a 404, a rate limit, an empty result, or a malformed response, and giving the agent the right signal to retry, fall back, or escalate. Good tool result parsing also involves truncation: a database query that returns 10,000 rows must be summarized before being fed into the context window, or the agent's short-term memory fills up. The design of the parsing layer — what gets extracted, how errors are represented, and how results are formatted — directly determines how reliably an agent can reason about tool outputs and decide what to do next. This unglamorous engineering layer is responsible for a disproportionate share of agent reliability issues in real deployments.