harrison chase

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—because agent architectures become obsolete every 9 months.

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 datasets capture real-world usage to guardrail future prompt changes—shipping iteratively with limited blast radius beats months-long waterfall builds.

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 step-change productivity gains in enterprises where events fire constantly.

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 streams (email, Slack, etc.) will deliver step-change productivity by eliminating copy-paste workflows.

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 as capabilities leapfrog is necessary—architectures from 18 months ago are already obsolete for complex tasks.

Production agent systems will compose interchangeable harnesses, secure runtimes, and heterogeneous model fleets (frontier orchestrators, open sub-agents, fine-tuned specialists) to balance capability, cost, and data sensitivity.

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 advantages makes them viable for always-on, high-frequency sub-agents.

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 leading indicator for agent progress.