8 components of harness engineering: why the system around the model, not the model, makes agents reliable
blaizedsouza · x · 2026-09-19
Nikki Siapno lays out 8 components of "harness engineering": a model that reasons, generates, and plans isn't automatically a reliable agent — the surrounding system determines what it can access, what it can do, how it recovers from failure, and whether it improves over time.
Key components:
- Context: files, docs, retrieved knowledge, user input, app state for the current task
- Memory: carrying decisions, failures, and fixes across steps or sessions
- Tools: APIs, database queries, code execution, file search, resource editing
- Orchestration: coordinating the agent loop — what's next, which tools, when to retry
The post also links DeepLearning.AI's Oracle-partnered short course Building Adaptive AI Agents, covering three adaptation layers: turning agent traces into reusable human-approved skills, and building a code knowledge graph as retrieval context that beats keyword search.
More from coding & agent
- Solo operator's AI agent runs a paid-deed platform end-to-end: 6 real payouts across 5 countries — reckon369 · 2026-09-19
- Zuck Opens Muse Connectors: Consumer Apps Enter the Agent Era — davidyin44 · 2026-09-19
- Meta's Muse agent lands on Mac, acting in files, messages, calendar and email — emmanuelvivier · 2026-09-19
- Developer on multi-agent coding: parallel solution search beats one big model — Aryvyo · 2026-09-19
- François Fleuret: ChatGPT Built His Local LLM Server, Then Swapped to the 6GB Model — francoisfleuret · 2026-09-19
- Squad lets you stack existing ChatGPT, Claude and Grok subscriptions to power AI teammates — tibo_maker · 2026-09-19