Joojourr

All is about probability!

[Alex @de1lymoon]

OpenAI pays $700K+/year to engineers who know how to build the harness around AI agents

In this free 52-minute course, engineers from OpenAI, FactSet, Nubank and Snowglobe revealed the full Agent Harness Engineering roadmap:

• step 1 → the model only proposes an action. the harness controls state, tools, permissions, retries and whether that action actually happens

• step 2 → most agent failures are infrastructure failures: corrupted state, overlapping writes, unfinished tool calls and missing stop conditions

• step 3 → skills are becoming the new product features. instead of hardcoding workflows, teams build reusable capabilities that agents can discover and execute

• step 4 → every skill needs its own contract, versioning and eval suite. one model update can silently break an entire production workflow

• step 5 → simulate thousands of multi-turn interactions before deployment. Nubank and Snowglobe use this loop to find failures early and ship agents 20× faster

52 minutes replaces a $500 Agent Engineering course

bookmark & watch, then read the full Agent Harness playbook below ↓

[diva @divaagurlxw]

As an AI Engineer. Please learn

>Harness engineering, not just prompt engineering

>Context engineering, not just long prompts

>Prompt caching vs. semantic caching tradeoffs

>KV cache management, eviction, reuse, and memory pressure at scale

>Prefill vs. decode latency and why they optimize differently

>Continuous batching, paged attention, and throughput optimization

>Speculative decoding vs. quantization vs. distillation tradeoffs

>INT8, INT4, FP8, AWQ, GPTQ, and when quantization hurts quality

>Structured output failures, schema validation, repair loops, and fallback chains

>Function calling reliability, tool contracts, argument validation, and idempotency

>Agent guardrails, loop budgets, tool budgets, and termination conditions

>Model routing, graceful fallback logic, and degraded-mode UX

>RAG architecture: chunking, embeddings, hybrid search, reranking, and freshness

>Retrieval evals: recall, precision, grounding, attribution, and citation quality

>Evals: golden sets, regression tests, adversarial tests, LLM-as-judge, and human evals

>LLM observability as a first-class discipline: traces, spans, tokens, latency, errors, and drift

>Cost attribution per feature, workflow, tenant, and user journey not just per model

>Safety engineering: prompt injection defense, data leakage prevention, and permission boundaries

>Multi-tenant isolation, cache safety, and cross-user context contamination prevention

>Fine-tuning vs. in-context learning vs. RAG vs. distillation and when each is the wrong tool

>Latency, quality, cost, and reliability tradeoffs across the full inference stack

>Production failure modes: hallucinated tool calls, malformed JSON, stale retrieval, runaway agents, and silent eval regressions

[Joojourr @jonathan_tarman]

@skyholicc88 @skyholic888 TN5YTjiQgAS3iQwumQVDGGALQRtMgpL79K

Iziinn, thankk youu!!