# The Chronicle Lab

**Focus:** turning everyday communication into structured memory.

We examined the **OpenAI Agents SDK** and the **Agent Builder Kit** and pushed on our Slack automations that digest research and (eventually) produce a newsletter. It’s easy to make a single emoji-triggered summary; the hard part is **modeling complex logic** across threads, channels, sources, and time.


The team did a close reading of the Agent Builder to ask: what would an Airtable look like that can actually capture that orchestration logic? At the same time, we needed to prototype quickly in Slack. That produced the core **tension** of this lab:

- Designing a **complex, “right” relational database** that can express agent workflows;
- While needing to **test behaviors now** with lightweight bots.

The result is a **complex database and simpler agents** for the moment: the database expresses the shape of where we’re going; the agents stay lean so we can iterate.

**Deliverables:**
1. A [structured Airtable schema](https://airtable.com/appWh4ua547sXqZxG/tblfSs46SKQ8LPtUp/viwot62XXGQVBVmrg) that encodes agentic logic and future workflows.


2. A simple “Colbert bot” prototype that fires on emoji and responds (joke/summary), illustrating fast iteration while the schema matures.


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### Tools Used
- **OpenAI Agents SDK** — multi-agent orchestration
- **Airtable** — relational database for logs and logic
- **Slack API** — emoji triggers and contextual automation

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### What We Learned
- Plan the **data model** to match agent workflows, but keep initial bots **small and testable**.
- Expect a staged path: **schema first**, then progressively smarter agents plugged into it.