Thoughts

The transition from stateless LLMs to agentic workflows is fascinating. A simple Chain-of-Thought (CoT) prompt can dramatically increase reasoning capabilities, but true agents need memory persistence to be token-efficient.

interface AgentState {
  memory: Array<Message>;
  contextWindow: number;
}

I've been experimenting with the chatty-agent protocol for clustering issues, and the preliminary results are incredibly promising. Cutting down investigation times from 18 hours to 30 minutes is a game changer for on-call engineers.

Durable execution (Temporal, Restate, Cloudflare Workflows) is rapidly becoming the standard architecture for multi-step AI agent workflows.

When an AI agent task takes 3 minutes and involves multiple external API calls (searching PubMed, fetching protein structures, running code sandboxes), traditional HTTP request-response handlers fail:

1. Timeouts: Cloud gateways drop connections after 30-60s.
2. Process Kills: Pod restarts lose all transient state in memory.

By structuring agent loops as deterministic steps with event-driven state persistence, your agent can sleep during long tool executions and resume instantly without losing context.