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Coding agents control robots through ROS 2
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◆ Braid Daily · 2026-10-05

Coding agents control robots through ROS 2

OpenRUA puts robot controls in an agent’s terminal; DeReAct separates action approval from completion checks.

Conceptual artwork of a robotic gripper above a cube and dish, with yellow light on a charcoal work surface.

The lead

1

Following yesterday’s discussion of agent harnesses, OpenRUA gives Claude Code with Opus 5 terminal access to ROS 2 to write its own perception and control programs. The authors report 99% success on CaP-Bench and 87% on LIBERO-PRO in simulation, without task-specific training or bespoke robot primitives.

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What the harness still controls

3

DeReAct separates action approval from completion checks

arXiv

Across GAIA and SWE-bench Verified, separate action and completion policies improve Sonnet 4.5’s first-attempt pass rate by 4.2–5.2 percentage points. With Opus 4.5, the pass rate stays comparable to ReAct, while execution records contain more complete evidence and better satisfy constraints.

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Agents create and load their own tools at runtime

AI Engineer / AWS

This AWS talk shows agents writing and loading tools at runtime, then creating specialized sub-agents. The design starts with an editor, a shell, and a loader; the speaker emphasizes explicit token limits and evaluation.

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Agents in operating workflows

3

Sourcegraph takes batch changes across repositories

AI Engineer / Sourcegraph

Sourcegraph describes a beta tool that rolls out changes across hundreds or thousands of repositories and responds to CI results and PR comments. In early Mercari testing, the company reports patching a GitHub code-injection vulnerability in 80 additional repositories beyond the initial targets.

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AssemblyAI reports 80% support resolution for Joey

AI Engineer / AssemblyAI

AssemblyAI reports that its Joey support agent reached 80% end-to-end resolution within a week, at roughly $700 a month for tokens and inference. The agent uses synced local Markdown documentation and escalates the remaining inquiries to humans.

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PayPal tests richer product catalogs for agent discovery

AI Engineer / PayPal

PayPal reports that targeted catalog enrichment improves agent recommendation accuracy, with the largest gains for products whose data is sparse. Excess unstructured text and marketing boilerplate can dilute semantic retrieval and reduce performance.

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Models and inference costs

2

Most reference tokens match a small model

arXiv

A 500-million-parameter model reproduces 92–95% of reference tokens when given the correct preceding tokens. A routing experiment lowers projected latency from 7.59 to 5.12 seconds. The agreement rate measures token reproduction under that condition, rather than independent answer quality.

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Regulation, political spending, and measured harms

4

Companion episode

The Agent With a Terminal and Nothing Else

· 00:28:23