The Agent Stack
Vinoth Govindarajan, OpenClaw UnboxedThe Agent Stack is a technical deep dive into how modern AI agents actually work under the hood, from control loops and memory systems to orchestration, evaluation, and production deployment.
| Platform |  | Pricing | Only free issues | Publishes | Weekly |
|---|---|---|---|---|---|
| Issues | 19 | Founded | 5 months ago | Last Issue | 4 days ago |
| Active | |||||
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The Data Agent Stack - Part 4: Meaning Lives in Code
A user asks for active users.
The agent finds a table with user_id, event_time, and platform.
The SQL runs. The chart looks reasonable.
But the table excludes logged-out traffic. It deduplicates at session grain, not user-day grain...
The Data Agent Stack - Part 3: Context Assembly for Data Agents
A user asks:
“Why did weekly active usage dip after the launch?”
The agent finds a canonical metric definition, an older dashboard query, a human annotation about test tenants, a launch document, a stale incident note, a saved correction,...
The Data Agent Stack - Part 2: The Data Foundation Is the Agent
A coding agent starts with a repo.
A data agent starts with organizational entropy.
The user asks, “What was weekly active usage after launch?”
The platform has five plausible tables, three similar columns, two dashboards, one stale doc,...
The Data Agent Stack - Part 1: What Is a Data Agent?
Most data-agent demos start with a simple promise:
Ask a question in English.
Get an answer from your warehouse.
Thanks for reading The Agent Stack! Subscribe for free to receive new posts and support my work.
That is useful.
But it i...
The Agent Stack - Part 8: Observability, Evaluation, and Production Feedback Loops
A demo is allowed to be a story.
Production is not.
At 3:07 AM, the question is not whether the agent looked smart in the demo. The question is why a customer received the wrong refund, which run did it, what context it used, which tool a...
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Authors
The writers behind this newsletter.
Systems engineer at OpenAI, open-source contributor, and co-author of Engineering Lakehouses with Open Table Formats, explaining how modern AI agents and data systems actually work in production.
Unbox the AI that actually does things. Build cheaper more capable agents and smarter routing for token efficiency. Copy the configuration. Run your 24/7 team. An independent AI agent community by Josh Davis
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