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.
Engineer LLM jailbreak defenses around real outcomes rather than refusal wording. Build a threat model, constrain tools and data, evaluate adaptive multi-turn attacks, calibrate automated judges against human labels, measure false refusals, and turn incidents into regression tests.
Build a reproducible Agent evaluation harness that resets state, runs repeated trials, replays tools, injects controlled faults, grades outcomes and trajectories, calibrates model judges, enforces budgets, and blocks unsafe regressions without storing 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.
Design LLM guardrails that control prompts, retrieval, model output, tools, and side effects. Learn policy placement, Go enforcement, evaluation, and rollout.
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.
Build an evidence-based AI crawler policy that separates search indexing, model training, and user-directed agents. Learn RFC 9309 rules, verified bot identity, enforcement, rate limits, Content Signals, AI Labyrinth, logging, licensing, and rollout tests without treating robots.txt as access control.
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.
Turn the amended EU AI Act into an auditable engineering program without pretending code can decide legal scope. This checklist covers the 2026 Digital Omnibus timeline, operator roles, intended-purpose classification, Article 50 transparency, high-risk evidence, logging, human oversight, FRIA boundaries, conformity routes, post-market monitoring, and a Go release gate for versioned evidence.
Understand DeepSeek Harness as a preview Agent runtime: Cordis plugins, launch profiles, product modes, append-only session evidence, capability seams, desktop and Web entry points, security boundaries, and a Go adoption gate.
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.