SourceMar 26, 20262026-03-263 minVerified
Rethinking LLM Agents as Adaptive Computation Graphs
View LLM agents as runtime-adaptive computation graphs to optimize accuracy, cost, latency, debugging, and control.
View LLM agents as runtime-adaptive computation graphs to optimize accuracy, cost, latency, debugging, and control.
Defines skills as executable function code and manages them online via create-run-update-on-fail-save-on-success loops.
A practical look at memory admission control for LLM agents, reducing long-term memory pollution while improving auditability and metrics.
A field report from running a community bot: what automation can do, and what still requires human operational control.
Analyze how OpenAI's Responses API and MCP reduce AI agent latency and improve cache efficiency through server-side state management.