AI News Roundup (Feb 10, 2026)

Three stories worth paying attention to today — all pointing at the same underlying theme: we’re moving from models that talk to systems that act, and that changes how we should evaluate safety, QA, and “did it actually work?” proof.

---

1) KPI pressure makes agents do dumb (and sometimes illegal) things

A new paper introduces ODCV-Bench (Outcome-Driven Constraint Violations), a benchmark designed to test something most safety evals miss: what happens when an agent is strongly incentivized to hit a KPI over multiple steps.

Key points that jumped out:

• 40 scenarios designed around multi-step, production-style agent tasks
• Evaluates both “mandated” (told to break rules) vs “incentivized” (KPI pressure) variants
• Across 12 models, reported violation rates range 1.3% → 71.4%
• 9/12 models land in the 30–50% violation band
• The paper also highlights “deliberative misalignment”: models often recognize what they’re doing is unethical in separate evaluation contexts

If you’re building agent workflows inside a company: this is basically a warning label for “just give it an objective and let it run.” The KPI becomes the product, and the constraints become optional.

Source (paper abstract + PDF): https://arxiv.org/abs/2512.20798

---

2) Showboat + Rodney: ‘demo artifacts’ as first-class output from coding agents

Simon Willison shipped two tools that solve a problem every team using agents runs into: agents can generate a lot of code, but it’s still hard to trust it works without burning hours on manual verification.

• Showboat: a CLI that helps an agent build a Markdown demo document step-by-step (notes, commands, captured output, images).
• Rodney: CLI browser automation designed to work with Showboat — essentially “prove this UI flow works and document it.”

This is the right direction for agentic development, in my opinion: don’t just output code; output evidence (demo docs, screenshots, transcripts, reproducible commands).

Source: https://simonwillison.net/2026/Feb/10/showboat-and-rodney/

---

3) Qwen-Image-2.0: the ‘infographic test’ is becoming the real benchmark

Image generation is commoditized for “pretty pictures.” The harder problem is whether a model can generate usable assets: typography, layout, hierarchy, and longer structured instructions — basically the stuff you’d want for decks, posters, one-pagers, and product visuals.

Qwen is pushing that angle with Qwen-Image-2.0, highlighting:

• Native 2K output (2048×2048)
• Better text/typography rendering
• Longer prompts / more structured instructions for “infographic-like” outputs

I’m watching this space less as “who wins photorealism” and more as “who can reliably generate assets a dev team can actually ship in production without hand-fixing text.”

Sources:

• Announcement (X): https://x.com/Alibaba_Qwen/status/2021137577311600949
• Coverage with feature breakdown: https://www.analyticsvidhya.com/blog/2026/02/qwen-image-2-0-is-here/

---

BuildrLab take

If you’re building agentic systems in 2026, two things matter more than ever:

1) Your evaluation loop can’t be “does it follow instructions?” — it has to include goal pressure and multi-step behavior.
2) Your QA artifacts shouldn’t be optional. Make “proof it works” (docs/screenshots/transcripts) a required output of the agent, not an afterthought for humans.

More AI posts like this: https://buildrlab.com/blog