The Harness-Model Training Loop: Why the Boundary Between Agent Infrastructure and Model Weights Is Collapsing
Open models reaching agent parity, task-specific harness engineering, and trace-driven fine-tuning are merging what used to be separate concerns into a single iterative loop — with major implications for how teams build and operate agents.
What's Happening
Analysis
Sequenced Pipelines: How Structured Handoffs Improve Multi-Agent Systems
How sequenced specialist agents with defined handoff contracts and backward feedback loops produce more reliable results than flat swarms or orchestrator/worker splits.
April 2, 2026When Evals Become Optimization Targets
Optimizing agent harnesses against a fixed eval suite triggers Goodhart's Law — the same dynamic that eroded search quality through SEO. How adversarial eval co-evolution can help.
March 28, 2026Strategic Forgetting in Agent Memory Systems
Production experience and neuroscience research both suggest that selective forgetting — not total recall — is a key architectural primitive for agent memory.
March 28, 2026When the Harness Becomes the Differentiator
As model reasoning converges across providers, the competitive edge in agent systems shifts to harness engineering — middleware, evals, memory, and environment design.
March 28, 2026Coding Agent Runtimes Go Full-Stack
In a single week, sandboxes, subagents, deployment CLIs, and control planes all shipped across major platforms — tracing the shape of a full managed runtime.
March 21, 2026The Emerging Coding Agent Runtime Stack
Sandboxes, subagents, and deploy CLIs are converging into a recognizable runtime stack for coding agents. A look at how the layers are forming.
March 19, 2026From the Archive
The Agent Harness as Execution Environment
Agent infrastructure is converging toward harness-as-runtime architectures with context compression, checkpoint debugging, and self-healing memory.
March 14, 2026Evaluate-as-Action: Teaching RAG Agents to Judge Their Own Retrieval
How making retrieval quality assessment an explicit agent action—rather than an implicit assumption—improves multi-hop reasoning and enables process-level reward shaping for RAG agents.
March 11, 2026LDP: Identity-Aware Communication for Multi-Agent LLM Systems
How the LLM Delegate Protocol makes agent identity, trust, and provenance first-class primitives in multi-agent communication.
March 11, 2026