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AI Infrastructure · Modular agent stack—harness (Deep Agents/LangGraph), runtime (OpenShell), model routing (frontier + open + fine-tuned)—emerges as standard architecture
now playing · AI Infrastructure
Enterprise AI Adoptiontailwindscore 8/10harrison chase
Eval-driven development and observability are prerequisites for enterprise trust in autonomous agents
Enterprises gain trust not through exhaustive upfront testing but by shipping fast with small eval sets (5-10 cases), observing real runs via tools like LangSmith, and iterating weekly—beca…
Enterprise AI Adoptiontailwindscore 8/10harrison chase
Evaluation-driven development with small eval sets (5-10 cases) is the practical path to enterprise trust in agents
Enterprises gain trust through observability (LangSmith) and eval-driven development; starting with just 5-10 scenarios forces product thinking about desired agent behavior, and living eval…
AI Agentstailwindscore 8/10harrison chase
Asynchronous sub-agents and always-on event-driven agents will unlock massive enterprise productivity
Agents that run persistently in the background, listen to events (emails, triggers), and spin up long-running sub-agents asynchronously will replace manual copy-paste workflows, delivering…
AI Agentstailwindscore 8/10harrison chase
Async sub-agents and always-on event-driven agents to unlock massive enterprise productivity
Harrison Chase predicts asynchronous sub-agents managed by an orchestrator will become the dominant coding agent paradigm within months, while always-on agents listening to enterprise event…
AI Infrastructuretailwindscore 7/10harrison chase
Modular agent stack (model + harness + runtime) with open runtimes like NVIDIA OpenShell enables flexible deployment across environments
The agent stack separates into model, harness (Deep Agents, LangGraph), and runtime (OpenShell, GPU cloud, local); picking best-of-breed per layer and swapping harnesses every 9-12 months a…
AI Infrastructuretailwindscore 7/10harrison chase
Modular agent stack—harness (Deep Agents/LangGraph), runtime (OpenShell), model routing (frontier + open + fine-tuned)—emerges as standard architecture
Production agent systems will compose interchangeable harnesses, secure runtimes, and heterogeneous model fleets (frontier orchestrators, open sub-agents, fine-tuned specialists) to balance…
Open Source AItailwindscore 7/10harrison chase
Open models like Qwen Coder now reach capability threshold to drive general-purpose agent harnesses
Coding-optimized open models (e.g., Qwen Coder) outperform base versions on agent harnesses because harnesses resemble coding environments (file systems, bash tools); this plus cost advanta…
AI Coding Agentstailwindscore 7/10harrison chase
Coding ability is the hidden proxy for general agent competence in file-system-based harnesses
Because agent harnesses expose file systems and bash tools, models trained for coding (Qwen Coder, Claude Code) naturally excel at driving any tool-using agent, making coding benchmarks a l…
AI Agentstailwindscore 7/10harrison chase
Agent memory and persistent identity emerge as critical next frontiers for enterprise adoption
Agents that remember interactions, update their own skills, and operate as persistent identities (like 'Tom the marketing agent') with their own credentials—rather than acting on behalf of…