Agents in Production: How OpenGov Built and Scaled OG Assist - Gabe De Mesa, OpenGov
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AI Engineer
Author
AI Engineer
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Terms in this piece · Glossary
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
agentic loop — The cycle an agent runs in: decide, call a tool, read the result, decide again — repeating until the goal is met or a stop condition fires.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
Why it matters
A hands-on look at running agents in production at scale — covering the agent harnessThe scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.Full definition →, A2A protocol, evals, long-context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition → handling, and observability from a team serving thousands of government deployments, useful if you're moving agents past prototype into real workloads.
Key quotes
“we decided to move over to our own kind of Effect Native Agent Loop to have full regency over this uh Agent Loop”
Gabe De Mesa
“always always always making sure that, um, humans are in the driver's seat”
Gabe De Mesa
“we found that having some sort of um rolling summarization was more effective than you know always stuffing in the latest and most recent uh messages”
Gabe De Mesa
“we believe that tools and skills are really all you need”
Gabe De Mesa
“we're building tools and skills for customer-facing uh agents and and that has been great, but we're also building them internally as well to help accelerate our development workflows”
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