If 2025 was beginning of agents, 2026 will be around Agent Harnesses. An Agent Harness is the infrastructure that wraps around an AI model to manage long-running tasks. It is not the agent itself. It operates at a higher level than agent frameworks. The harness provides prompt presets, opinionated handling for tool calls (Human in the loop), lifecycle hooks or ready-to-use capabilities like planning, filesystem access or sub-agent management. As Benchmarks are going to become more complex we need to bridge the gap between benchmark claims and user experience. A Agent Harness can be essential for three critical reasons: - Validating Real-World Progress: Allows users to easily test and compare how the latest models perform against their use cases and constraints. - Empowering User Experience: Without a harness, the user's experience might be behind the model's potential. - Hill Climbing via Real-World Feedback: A shared, stable environment (Harness) creates a feedback loop where researchers can iterate and improve ("hill climb") based on actual user adoption. We are heading toward a convergence of training and inference environments. We see a new bottleneck being context durability. The Harness will become the primary tool for solving "model drift”. Read my full blog 🔽