A production-focused series on LLM evaluation, governance, and security, covering Harness Engineering, LLM-as-a-Judge, red teaming, prompt injection defense, guardrails, jailbreak analysis, OWASP Agentic Top 10, audit trails, and quality gates.
Define Harness Engineering for AI agents without mechanical-engineering ambiguity. Learn how runtime policy, tool governance, state, budgets, approvals, observability, evaluation, and recovery bound model-driven actions, and where prompts, MCP, sandboxes, DevOps, architecture, implementation, and testing fit.
Build an Agent Harness with durable state, policy gates, MCP adapters, approval-bound effects, idempotent retries, recovery tests, and auditable events.
Learn how to defend LLM applications against jailbreak attempts with layered guardrails, least-privilege tools, output controls, red-team evaluation, and incident response. Clarifies how jailbreaks differ from prompt injection.
Design a reproducible Agent Harness for AI systems. Learn how to isolate tools, replay scenarios, inject failures, enforce step and cost budgets, evaluate task outcomes and safety, and compare judge-assisted scoring without exposing private chain-of-thought.
Build a calibrated LLM-as-a-Judge pipeline beyond ROUGE and BLEU with judge contracts, swap tests, human controls, slice metrics, and production release gates.
A deep dive into LLM Guardrails principles and engineering. Covers NeMo Guardrails, Guardrails AI, and Llama Guard. Includes Python/Node.js examples for building safe, reliable, and hallucination-free AI applications.
A rigorous guide to evaluating language models when public scores are incomplete evidence. Learn how contamination, saturation, annotator disagreement, Goodhart effects, judge bias, and workload mismatch distort benchmarks, then build versioned task sets, deterministic oracles, calibrated human or model review, cost and latency measurements, and release gates.
Explore the escalating battle between AI web crawlers and content publishers. From traditional robots.txt to Cloudflare's AI Labyrinth and legal challenges, learn how the web is defending itself against unauthorized AI training data collection.
Design AI Agent memory around purpose limitation, data minimization, provenance, retention, access control, deletion propagation, and evidence. Explains why vector deletion, summaries, caches, backups, fine-tuning, and model outputs need separate treatment under GDPR-style privacy programs.
A practical EU AI Act engineering checklist covering applicability, risk classification, audit logging, evaluation, human oversight, technical documentation, and conformity-assessment evidence. Uses the Digital Omnibus timeline: Article 50 from August 2026, Annex III high-risk rules from December 2027, and Annex I product-embedded rules from August 2028.
Learn how DeepSeek Harness uses Cordis plugins, profiles, bundles, events, and patches to compose traceable agents, with practical design boundaries for production teams.
Build safer DeepSeek Harness plugins with Cordis apply functions, dependency injection, reversible effects, tool policy hooks, configuration overlays, and upgrade tests.
Understand DeepSeek Harness session logs, trajectory inspection, fork and replay semantics, durable event design, context reconstruction, and the limits of replay for external side effects.