Reliability & Recovery
+Progress signals, checkpointing and recovery from authentication or provider waits.
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We Removed Middleware From Our WordPress AI Workflow — What the Native REST API Already Gave Us
A practical case study: we moved routine AI-driven WordPress work onto the native REST API, tested posts, pages and media end to end, and kept privileged plugin deployment separate. Read the article →
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Your AI Agent Said It Finished. It Hadn’t.
An AI agent saying “done” is not evidence. Use external acceptance checks, durable receipts and independent verification before treating autonomous work as complete. Read the article →
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We Ran 12 GPT-6 Luna Low Sessions at Once — Here’s What Happened to CPU and RAM
One owner-reported, single-machine snapshot: CPU ~39–71%, RAM ~25.6–26.0 of 31.8 GB, GPU ~10%. This is not a concurrency ceiling or controlled benchmark. Read the article →
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AI Workflow Acceptance: A Heartbeat Is Not a Verified Result
Separate worker liveness, provider activity, durable progress and verifier acceptance; no production throughput rate is claimed. Read the article →
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WAIT_AUTH: Separate Authentication Recovery from Resource Waiting
Treat credentials and provider capacity as separate wait states, preserve the checkpoint and measure human recovery rather than guessing. Read the article →
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A Plan Change Coincided with OpenCode Auth Failures: What the Logs Actually Show
Redacted logs show four auth-invalidated records across two sessions; timing is measured, but the plan-change cause and resumed jobs remain unknown. Read the article →
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WAIT_RESOURCE: What a Provider-Free Resume Test Can—and Cannot—Show
A fake-adapter wait test can exercise checkpoint and retry invariants; it cannot establish live provider recovery or accepted output. Read the article →
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Surviving AI Usage Limits: Keep Recovery Outside the Model Turn
Persist job state outside a constrained model request and test resource-wait invariants without mistaking simulation for production evidence. Read the article →
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How to Detect a Stalled AI Agent Without Killing a Legitimately Long Job
Separate process liveness, provider activity, semantic progress and acceptance so quiet legitimate work is not mistaken for a stall. Read the article →
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A live heartbeat is not model progress: lessons from a stalled canary
A reported >60-minute canary stall shows why wrapper liveness, provider activity, task progress and verification must be tracked separately. Read the article →