harness-engineering · YT Insights Explorer

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Harness Engineering: How to Build a Software Factory — Dru Knox, Tessl

How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen

Can Rewriting an AI Agent Bend the Intelligence Curve? - Zhengyao Jiang

Unhobbling Claude, the difficulties and processes of building agents - Thariq Shihipar, Anthropic

The Dark Arts of Skill Engineering — Paul Bakaus, Renaissance Geek (Impeccable)

Harness Engineering: Building the Production Cage for Powerful Domain Agents — Mike Chambers, AWS

What Building an AI Scientist Actually Requires Beyond Intelligence — Edward Hughes

CMU AI Agents 2026: 1. What are Agents and How Do They Work?

How Anthropic Builds And How Engineering Will Change Soon | Thariq Shihipar

Continual Learning: How AI Agents Get Better With Every Use | Arjun Karanam, Trajectory

Improving Agents is a Data Mining Problem — Vivek Trivedy, LangChain

The Art of Loop Engineering: How to Build Agents That Improve Over Time

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Related tagged insights

Harness Engineering: How to Build a Software Factory — Dru Knox, Tessl

Harness engineering …

The three factory me…

The agentic stack is…

Adoption of harness …

Progress is instrume…

Codifying core playb…

How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen

Dialog-RSN-1 splits …

Turn boundaries and …

Enterprise different…

Full end-to-end S2S …

Latency in voice age…

Agent-generated outp…

Robust voice models …

Can Rewriting an AI Agent Bend the Intelligence Curve? - Zhengyao Jiang

AIDE separates the o…

The outer loop auton…

Search policy itself…

Reward hacking rate …

Harness engineering …

Recursive self-impro…

A division of labor …

Unhobbling Claude, the difficulties and processes of building agents - Thariq Shihipar, Anthropic

Capability overhang:…

System prompt design…

The unknown matrix (…

Browser agent harnes…

Emergent tool use ap…

Agent development sh…

The Dark Arts of Skill Engineering — Paul Bakaus, Renaissance Geek (Impeccable)

Self-critique by the…

Latent-space bias to…

Guardrails should be…

Sub-agent spawning i…

A reactive loop turn…

Divergence is produc…

Harness Engineering: Building the Production Cage for Powerful Domain Agents — Mike Chambers, AWS

An agent decomposes …

In the AgentCore arc…

The harness componen…

Distinction between …

'No more slop ops' —…

Local prototyping is…

What Building an AI Scientist Actually Requires Beyond Intelligence — Edward Hughes

Long-horizon multi-t…

For non-verifiable a…

A 27B open-weight po…

Open-endedness is op…

Harness engineering …

Optimization is stru…

CMU AI Agents 2026: 1. What are Agents and How Do They Work?

Context compaction i…

Harness engineering …

Tool use is a typed-…

Agent deployments sh…

The ReAct loop is de…

The course enumerate…

Multiple parallel ag…

How Anthropic Builds And How Engineering Will Change Soon | Thariq Shihipar

Harness complexity s…

Verification code vo…

Loop engineering: re…

Artifacts as an outp…

Knowledge-work reduc…

Abstraction shift: t…

Continual Learning: How AI Agents Get Better With Every Use | Arjun Karanam, Trajectory

Trace fidelity is a …

Product interface af…

Evaluations must be …

The harness is a tra…

Open-weight models a…

Production agents em…

A packaged continual…

Improving Agents is a Data Mining Problem — Vivek Trivedy, LangChain

Agent runs are now l…

Cost-efficient trace…

Agent improvement sh…

Binary pass/fail age…

Continual learning f…

Agent engineering ca…

Autonomy is purchase…

The Art of Loop Engineering: How to Build Agents That Improve Over Time

Loops form a four-le…

A verification/gradi…

The hill-climbing lo…

Human oversight is t…

Engineering effort s…

Event-driven trigger…