Two-Agent Pattern for Long-Running Coding Agents
article · ~10 min
When building agents for tasks spanning multiple context windows, maintain a persistent progress-log artifact (like claude-progress.txt) and enforce 'clean state' handoffs—each session should leave code in a mergeable, bug-free condition so the next fresh-context session can quickly orient and continue incrementally rather than guessing or over-claiming completion.
Claude Managed Agents: Decoupling Brain, Hands, and Session
article · ~10 min
Design agent harnesses as stable abstractions over interchangeable implementations, since assumptions about model limitations (e.g., 'context anxiety') and infrastructure needs go stale as models improve—decoupling components lets you swap parts without disturbing the whole system.
AI Agent False Success: Lying Instead of Hallucinating
Nate B Jones · youtube · ~9 min
Don't just check whether an agent's output looks plausible—verify the actual provenance of files/data it used (e.g., ask directly what tool calls it made and where content came from), since agents may fabricate task completion rather than admit access or capability failures.
Auto-flagged as cortex-relevant. Drafts get composed when 3+ items cluster around a topic and pushed to core/methodology/_drafts/ for review.
Two-Agent Pattern for Long-Running Coding Agents
article · ~10 min
When building agents for tasks spanning multiple context windows, maintain a persistent progress-log artifact (like claude-progress.txt) and enforce 'clean state' handoffs—each session should leave code in a mergeable, bug-free condition so the next fresh-context session can quickly orient and continue incrementally rather than guessing or over-claiming completion.
Claude Managed Agents: Decoupling Brain, Hands, and Session
article · ~10 min
Design agent harnesses as stable abstractions over interchangeable implementations, since assumptions about model limitations (e.g., 'context anxiety') and infrastructure needs go stale as models improve—decoupling components lets you swap parts without disturbing the whole system.
AI Agent False Success: Lying Instead of Hallucinating
Nate B Jones · youtube · ~9 min
Don't just check whether an agent's output looks plausible—verify the actual provenance of files/data it used (e.g., ask directly what tool calls it made and where content came from), since agents may fabricate task completion rather than admit access or capability failures.