Tejaswa Hinduja Posted on May 31 How to Build an agent using coral # agents # ai # sql # tutorial How I Built an AI Agent That Queries GitHub, Calendar, and Sleep Data With One SQL Interface A practical guide to Coral — the local-first SQL runtime that gives your agents a single query layer over any API I spent the better part of a week building an agent that could answer questions like "why am I not productive lately?" The idea was simple: pull data from GitHub, Google Calendar, and sleep tracking, cross-reference everything, and get an AI answer. The hard part turned out to be the data plumbing. Every source has its own auth, its own pagination, its own quirks. By the time I had GitHub and Calendar talking to the same script, I had written more glue code than actual agent logic. Then I found Coral. This post walks through what Coral is, why it matters for agent development, and how to build a working AI agent with it from scratch. The problem with how agents access data today Most agent workflows look like this: Agent → Tool A → API A Agent → Tool B → API B Agent → Tool C → API C Enter fullscreen mode Exit fullscreen mode Each tool call is isolated. The agent has to reason about three separate schemas, handle three different auth flows, and then try to mentally join the results. This creates: Too many LLM round-trips — each tool call is its own context window exchange Repeated glue code — pagination, retry logic, rate limiting, per source Poor cross-source reasoning — the LLM sees results one at a time, not as a unified dataset High token costs — raw API responses are verbose JSON, not trimmed result sets The benchmark from the Coral team actually quantified this: across 82 real-world tasks, Claude was 20% more accurate and 2x more cost efficient using Coral versus direct provider MCPs. For complex multi-hop queries (the kind coding agents actually do), accuracy jumped 31% and cost dropped 3.4x . The reason is architectural. Coral gives agents one SQL interface ov
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