What we learn building, deploying, and operating AI Agents for real workloads — written up while the detail is still fresh.
A request-scoped agent dies when its process does. Wrap Hermes-driven workflows in durable execution and they survive crashes, waits, approvals, and schedules.
A request-scoped agent dies when its process does. Journaling every step makes LLM calls, MCP tool runs, and human-approval waits resumable.
Giving AI agents a broader, independently aggregated view of the web — without permanently binding discovery to a single provider.
Meta‑search cuts dependence on any single ranking engine. Setup, independent verification, and an architecture separating discovery, retrieval, and reasoning.
Designing a failover chain that actually survives a provider outage — a practical guide for teams running production AI agents.
One failed LLM provider shouldn't take your AI agent offline. Here's how to build a fallback chain with genuinely different failure domains.