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All digests tagged Harness Engineering
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Harness engineering is the infrastructure around LLMs, encompassing tools, context, guardrails, and telemetry. While LLMs introduce non-determinism, core software principles like CI/CD, security, and observability remain essential for building robust agents.
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This devlog details building a self-compacting Pi Agent to solve context window limits in autonomous agents. By giving the agent a dedicated tool and three compaction thresholds (Notice, Warning, Force), engineers can achieve reliable, out-of-loop agentic coding.
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Agent harness engineering is the discipline of turning a non-deterministic LLM core into reliable, repeatable output. The architecture focuses on the data layer, memory management, and robust workflow loops.
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Learn about Harness Engineering: the architectural pattern for building scalable, production-grade AI agents. We discuss separating memory, tools, and runtime components for multi-tenant deployment.
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Solving overhang is an engineering problem, and this is the work that we can do within our companies and within our agents... The most reliable browser agents that we see in production right now are often writing code alongside using the browser to actually automate a task.
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Improving agents is a data mining problem. The key to continuous improvement is collecting comprehensive agent traces, which serve as the substrate for observability and continual learning.
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Harness engineering is a discipline focused on building automated loops of checks and maintenance agents to delegate more work to AI. This process involves improving three dimensions: autonomy, automation, and quality.
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Learn how LinkedIn built a hiring agent using LangGraph to cut time-to-interview by 60%. We cover the shift from static workflows to advanced plan-execute-replan loops and essential harness engineering techniques.
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