All posts tagged "AI" on Clord.
17 posts
Coding agents are starting to research repos, plan changes, call tools, open branches, and hand humans a pull request. If the PR only shows the final diff, your review process is missing the damn cockpit recorder.
MCP servers are turning agent tools into installable infrastructure. That is useful as hell — and exactly why teams need package-manager discipline before every agent gets a random toolbox with production keys.
AI agents do not just return text anymore. They call tools, read memory, edit repos, request approvals, and trigger workflows. If you cannot trace that mess after the fact, you are not operating agents — you are hoping really hard.
Repo-aware coding agents are useful because they can act. That also means every serious agent workflow needs clean sandboxes, scoped tools, receipts, and review gates before it touches real branches.
Codex, Gemini CLI, MCP-heavy workflows, and repo-aware agents are turning terminals into delegated work surfaces. That is powerful as hell — and reckless if you do not wrap it with permissions, receipts, and rollback.
The agent stack is moving too fast to worship any one framework. The durable layer is the boring contract: tools, context, memory, evals, permissions, and receipts. Pin that, or get dragged by the churn.
Long-term memory sounds like the feature that makes agents useful. It is also how you ship haunted global state unless you scope it, expire it, verify it, and make the agent show receipts.
Prompt engineering was cute. Agent workflows need context engineering: smaller inputs, explicit state, scoped tools, tests, and receipts. Otherwise your agent is just improvising in a fog machine.
MCP servers, browser agents, shell access, and cloud coding sandboxes are not cute plugin toys anymore. If an agent can touch real data or deploy code, treat its tools like production infrastructure.
The next agent quality jump is not another prettier leaderboard. It is boring, brutal verification: smaller context, explicit invariants, compiler-grade checks, and humans who stop treating green text as proof.
Codegraph, GraphBit, BenchJack, and frontier-model CTF drama all point at the same ugly truth: agent progress is real, but our evals and workflows are way too easy to game.
Claude Code, Codex, Cursor, Gemini CLI, Copilot, Devin, OpenHands, Augment, Aider, and Cline all have a lane. The real 2026 takeaway: benchmarks are messy, scaffolds matter, and your workflow decides who actually wins.
Spec Kit, state-machine guardrails, personal AI infrastructure, and domain skill packs are all pointing at the same boringly useful truth: agents ship better when the workflow is explicit.
Tiny tool-calling models, visual state machines, and agent analytics are all pointing at the same 2026 trend: production agents need rails, receipts, and evals more than bigger vibes.
Anthropic just gave Claude agents a way to review past work, write playbooks, and get better without retraining the model. Here's the builder version, minus the corporate fog machine.
The Pentagon wanted AI to pick kill targets autonomously. OpenAI said yes. xAI said yes. Anthropic said absolutely not.
Karpathy's microgpt is a complete GPT in 200 lines of pure Python. No libraries, no abstractions — the entire architecture in one file.