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Software architecture for AI-native apps - orchestration layers, context engineering, and persistent harnesses.
7 resources - 7 posts

A new paper proposes inverting traditional agent architecture - making the append-only event log the source of truth, not an afterthought. HN debates whether this is novel or just CQRS with extra steps.

Dan Abramov's explainer on ATProto architecture is making the rounds. The core insight: Bluesky's protocol separates hosting from applications in a way that Mastodon-style federation fundamentally cannot. Here's what that means for developers.

A practical architecture for multi-step Claude agents. Loop patterns, state management, error recovery, and the production gotchas that turn a five-step demo into a 20 percent success rate at scale.

The math of agent pipelines is brutal. 85% reliability per step compounds to about 20% at 10 steps. Here is why long chains collapse in production, and the six patterns the field has converged on to fight the decay.
Martin Fowler reframes AI-era debt into three layers - technical, cognitive, and intent. The third one is the one most teams are silently accumulating. Here is what it is and how to diagnose it.

From single-agent baselines to multi-level hierarchies, these are the seven patterns for wiring AI agents together in production. Each with a decision rule, an implementation sketch, and the tradeoffs that actually matter.

CloudFlare, Anthropic, and Cursor independently discovered the same pattern: don't load all tools upfront. Let agents discover what they need. The results are dramatic.
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