Luis Valencia @levalencia

3/ Once that happens, the question changes. It is no longer only: "Can it generate a patch?" It becomes: "Can we safely wire this into how software is delivered?"

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Luis Valencia @levalencia

a week ago

2/ Larger context windows matter. Configurable reasoning matters. But the Agent Tasks REST API may matter more, because it makes the agent something other systems can start and track.

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The next wave of agent tooling looks very boring, in a good way: context compression spec-driven development adaptive planning benchmarks reusable skills security harnesses Less "bigger prompt." More "better operating system around the agent."

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Luis Valencia @levalencia

a week ago

"Fix with Copilot" for failing GitHub Actions is a small feature with a big direction of travel. Agents are moving closer to the CI/CD loop. That is useful. Also risky if teams treat CI as a place where autonomous patches magically become safe.

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Luis Valencia @levalencia

a week ago

Coding agents do not just need better reasoning. They need: permissions logs budgets task queues CI checks rollback human approval The demo is "agent writes code." The enterprise version is "agent operates inside a controlled software delivery system."

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Luis Valencia @levalencia

a week ago

The interesting GitHub Copilot update is not just larger context windows. It is the Agent Tasks REST API. Once coding agents can be started and tracked through an API, they stop being only chat tools. They start looking like programmable SDLC workers.

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Luis Valencia @levalencia

2 weeks ago

Autonomous security agents are one of the highest-value, highest-risk agent categories. Anthropic's reference harness is useful because it shows the workflow shape: threat model, scan, triage, report, patch. But treat it as reference design, not production tooling.

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Luis Valencia @levalencia

2 weeks ago

A useful pattern in today's AI tooling signal: GitHub Copilot agent tasks: programmable work spec-kit: better task definition Headroom: context compression AdaPlanBench: adaptive planning evals DataCOPE: reusable skills The agent stack is getting boring in exactly the right place

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Luis Valencia @levalencia

2 weeks ago

Anthropic's "When AI builds itself" is worth reading less as AGI philosophy and more as an operations problem. Self-improving loops need: evals traces stop conditions review points limits on action Otherwise "improvement" becomes a very confident feedback loop.

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Luis Valencia @levalencia

2 weeks ago

The production question for coding agents is not "can it write code?" It is: Who triggered it? What context did it use? What permissions did it have? What did it change? Which tests ran? Who reviewed it? Can we roll it back? Agents need control planes, not vibes.

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Luis Valencia @levalencia

2 weeks ago

The interesting Copilot update is not only the 1M token context window. It is the Agent Tasks REST API. Once a coding agent has an API, it stops being just "chat in the IDE" and starts becoming programmable infrastructure inside the SDLC. That is a much bigger shift.

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Luis Valencia @levalencia

2 weeks ago

A lot of agent tooling signal is converging on the same boring problem: context. Compress it. Filter it. Spec it. Plan against it. Update it when constraints change. Bigger prompts help. Better context operations may matter more.

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Luis Valencia @levalencia

2 weeks ago

Anthropic’s autonomous vulnerability discovery/remediation harness is interesting less as a “use this tool now” repo and more as a workflow sketch. Threat model. Scan. Triage. Report. Patch. Review. Before automating the loop, decompose the loop.

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Luis Valencia @levalencia

2 weeks ago

Self-improving agents are easy to discuss in sci-fi terms. The practical version is less dramatic: evals audit logs human review stop conditions permission boundaries clear rollback paths That is where the real work is.

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Luis Valencia @levalencia

2 weeks ago

The future of coding agents probably looks less like chat and more like a work queue. Tasks in. Agent plans. Tools run. Code changes. CI checks. Human review. Logs retained. Bad changes rejected. Boring architecture. Useful architecture.