Agent control layers

Agent teams with repeated 429s or overloaded internal services should put a small job queue between agents and shared APIs. Aquifer shows the shape of that layer: agents submit jobs through MCP or HTTP, the runtime stores them in SQLite, then per-upstream workers dispatch at configured request and concurrency limits. The example config sets api.openai.com at 10 RPS with 3 concurrent requests, api.stripe.com at 20 RPS with 5 concurrent requests, and an internal backend at 50 RPS with 10 concurrent requests.

A useful pilot would wrap one high-traffic agent tool behind a durable queue and measure failed calls, queue wait time, retries, and task completion. The test should include an upstream slowdown, since Aquifer’s design allows response headers to reduce the live rate under the configured ceiling. This is a practical support layer for teams whose agents already work but fail when many runs hit the same backend at once.

Coding-agent users still hit workflows that leave the editor: browser dialogs, installers, native apps, and OS permission prompts. clawdcursor makes a concrete desktop-control pattern credible by exposing local desktop actions through one MCP entry, trying the accessibility tree before OCR or screenshots, and sending every call through a safety check. Its compact surface has 6 tool groups, while the 94-tool surface remains available for compatibility and debugging.

The first adoption test should focus on a narrow desktop workflow with clear stop conditions, such as filling a local app form, saving a file through a native dialog, or completing a browser-based admin task. The evaluation should log which actions used accessibility metadata, which fell back to OCR or screenshots, which actions needed confirmation, and whether batching reduced tool-call count. This keeps desktop automation close to named user tasks and makes safety prompts part of the workflow from the first trial.

Long-running coding and corpus-grounded agents need a way to shed stale files, failed paths, tool noise, and distractor evidence. Context Sculpting tested a two-model harness where an outer model can pass through, rewrite context, roll back, or terminate between inner-agent turns. The feasibility signal comes with a clear cost warning: in one coding repair example, the control run passed in 7 turns, 42.7 seconds, and about $0.015, while the harnessed run also passed but used 12 turns, 566.9 seconds, about $1.06, and hit a max-turn guardrail.

A practical test should allow context rewriting only under explicit caps: maximum rewrites, maximum added latency, maximum spend, and a hard stop after repeated non-improving edits. The evaluation should compare pass rate, total turns, cost, and reviewer-visible trace quality on tasks known to contain stale context or distracting files. This makes context editing a measured intervention for specific long-run failures, with policy limits set before the agent starts.