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All digests tagged Prompt 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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AI News & Strategy Daily | Nate B Jones
Are your AI agents creating real value or just 'productivity theater'? Learn 4 principles for managing AI agents in Claude and Codex to ensure your focus remains on high-leverage work, not endless, low-value tasks.
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Agentic Engineering is the future of software development. Learn the five core concepts—Prompt, Context, Harness, Loop, and Graph Engineering—that power autonomous AI agents and automate the entire SDLC.
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Learn how to move from subjective 'vibe checks' to systematic AI evaluations (evals). This guide covers defining concrete pass/fail criteria, analyzing agent traces, and building robust eval templates using LLM judges and Decision Models.
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The era of 'token maxxing' is over. To prove AI's ROI, focus on headcount saved, not token spend. Use 'Sonnet maxxing' and structured meta harnesses to keep human engineers in the loop.
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Building rigorous evaluation systems for AI coding agents is complex. The talk details why public benchmarks fail and how to balance measurable vs. representative tasks, emphasizing observability and trace analysis.
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This workshop compares GPT-5.5, Kimi, DeepSeek, and Mistral across complex scenarios like RAG, tool calling, and vision. Learn how to use ASSERT and DSPy to automate model evaluation and optimize prompts for portability.
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Loop engineering designs systems where AI agents iteratively prompt, observe, and adjust toward a measurable goal. This process requires managing context, tools (MCP/APIs), and persistent state (spine).
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Learn how to build reliable AI agents for high-stakes environments. We cover the AI Engineering Loop: tracing, dataset curation, and integrating automated evaluation (Evals) into your CI/CD pipeline to prevent regressions.
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Evals are running tests on traces. They are essential because agents are non-deterministic, meaning the same prompt can give different answers. This process allows teams to measure reliability and prove that changes actually improve the agent.
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Context engineering is crucial for AI agents. Learn about context rot and four failure modes—Poisoning, Distraction, Confusion, and Clash—and how to improve agent reliability by deliberately pruning context.
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Learn how to 'actually tokenmaxx' LLMs. This guide details a systematic workflow using DSPy and shadow evaluation to cut prompt bloat by 51% while maintaining quality and reducing compute costs.
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A deep dive into Anthropic's Claude skill for automating AI eval design. While promising, the speakers argue the automated process is flawed, advocating for a 'data-first' approach over automated eval creation.
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The future of AI agents lies in specialization. We must move from 'god models' to thousands of hyper-scoped, domain-specific agents, requiring standardized runtimes and a microservices mindset.
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Discover how to improve browser agents without changing model weights using the AutoBrowse loop. This system pairs a Doer agent with a Teacher agent to generate reusable 'skill' files, dramatically increasing reliability and reducing operational costs on complex websites.
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An in-depth comparison of Opus 5 vs. Opus 5.5 reveals that while the newer model improves writing flow, both still struggle with 'slop' and require heavy human editing for technical content.
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Learn to cut AI costs by shifting focus from cost per token to cost per task. Discover advanced levers like Prompt Caching, Programmatic Tool Calling, and Batch APIs to optimize your AI workflows.
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The key architectural decision in enterprise AI is determining if knowledge belongs in the prompt (behavior), memory (facts), or weights (reflexes). This is not a ladder, but three distinct tools.
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AI News & Strategy Daily | Nate B Jones
Microsoft is deploying Autopilot, an always-on AI agent built on OpenClaw, across core workplace apps. The value lies in accessing proprietary data and executing ongoing assignments, requiring users to master defining objectives and context.
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The future of AI interaction is moving away from dashboards and UIs toward APIs and CLIs. Governance must be applied via Policy as Code, and value must be measured by a defined 'unit metric' or 'North Star.'
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