7 articles connected to this topic.
Prompt cache helps long AI agent runs stay fast and affordable, but only if prefix boundaries, replay state and tool context remain exact. OpenClaw's beta fix shows what to harden.
AI agent memory reliability depends on more than vector search. See how SQLite state, embedding batches and source-backed recall keep long-running agents useful.
A multi-channel AI agent fails in subtle ways: duplicate replies on Telegram, dropped final answers on Slack, lost context across reconnects. Here's why outbound delivery breaks and how OpenClaw v2026.5.28 hardens it.
Self-hosted Telegram bot polling silently dies when the main event loop stalls. v2026.5.12 moves ingress to an isolated worker with a durable local spool, so your AI agent stops missing messages.
Princeton researchers reveal that AI agent reliability improves at half the rate of accuracy. A 10-step agent workflow at 90% per-step reliability will fail over 6 times daily — and the industry has no good fix yet.
A series of outages hit Amazon's e-commerce platform in early March, including one directly tied to its AI coding assistant Q. The company is now enforcing a 90-day safety reset with mandatory approval gates for 335 critical systems.
If you can't see what your agents are doing, you can't trust them. Here's a practical mission control setup: runs, logs, failures, and artifact tracking.