Dekita
Notes on running an AI-assisted solo operation: what actually works, what quietly breaks, and how to tell the difference.
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Your agent loop will fail silently. Here are three checks that catch it.
A CTO ran 28 coding agents for 964 hours and closed the campaign with 12,074 end-to-end cases and zero gaps. Five things still broke, and none of them threw an error. Three checks for the failures your dashboard cannot see.
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I built an offline invoice sorter for freelancers, then measured whether local inference is actually cheaper
An offline invoice sorter for freelancers: local Gemma 2 2B through llama.cpp, no API key, nothing leaves the machine. Then I measured whether local inference is actually cheaper. It is not. The money is a wash. Here are the numbers, the dead ends, and what you actually buy.
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One prompt shouldn't own your cloud: scoping AWS Bedrock AgentCore credentials
A single prompt to an exposed agent was enough to take over every Bedrock AgentCore agent in an AWS account. Here is a copy-paste audit you can run today to scope your agent's IAM role down before that happens.
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Stop your agent from inventing APIs: rebuild the contract from a HAR file
REA went from unnoticed to six figures of GitHub stars in a week by wiring coding agents to reverse-engineering tools. The useful part is narrower than the hype: turn a browser capture into an evidence-backed API inventory. Here is the workflow, the evidence rules, and three things to settle before you install it.
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Your agent just added a package that does not exist — run this before npm install
In a 2025 USENIX study, 19.7% of package names suggested by code-generating models did not exist, and 43% of the fake names repeated on every run, which makes them pre-registerable. A dependency-free script that fails before install, not after.
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Your agent sandbox isolates it. It does not govern it — and two of your rules cannot exist in a sandbox.
Isolation decides what an agent can reach. It says nothing about what the agent may do once it gets there. The rules most teams actually need — cumulative limits and \'only after X, deny Y\' — need a policy layer, not a tighter container.
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Your agent did something — but nobody can say what. A practical audit trail pattern for agent tool calls
Agents that call tools in the wild are hard to debug. This post walks through a minimal, copy-paste-able Node.js pattern for emitting a structured audit log on every tool call, approval, retry, and outcome.
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Your coding agent is following rules that are no longer true. Here is the fix.
AGENTS.md and CLAUDE.md go stale without throwing an error. It goes stale by design. Your agent will faithfully follow an old command that no longer exists. Here is a doc-drift check and a verify-by-retry loop that keeps the instructions honest.
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Your coding agent is only trustworthy where you can inspect its work. Draw that line.
Stack Overflow's 2026 survey: 67% of developers use AI to generate code in areas they know, but only 20% use it in production. The line between them is verifiability. Here is how to draw it as a workflow rule instead of a feeling.
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Code review is your last line of defense against agent pull requests
Agent-authored PRs went from under 1% to 27.6% of GitHub pull requests in fourteen months. Acceptance rates look fine; the failures land in places review was never built to check. Here is what to gate on.
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Your agent loads every tool schema by default. Decide which ones it should see.
Pi 1.0 shipped deferred tool loading and a codemode sandbox on October 1, 2026. That turns tool visibility into a per-tool decision you own. Here is how to audit what your agent is loading, and why the answer changes cost and behaviour.
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Your agent loop has no cost ceiling. Here is the fix.
A runnable per-task token budget guard for agent loops, plus the routing and loop-detection rules that keep the cost bounded. Built while reading agent cost surveys and looking for where the ceiling should sit.
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Your agent's kill switch was never tested. That is the whole bug.
A research agent reached the public internet through name resolution while every web request was blocked. The detection worked in 15 minutes. Stopping the run took two and a half hours. Here is the default-deny egress setup that closes the class of bug, not just the one instance.
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Your coding agent will call a function that does not exist. Here is the fix.
Plausible code against an API that isn't there survives a quick review. The fix is not a better prompt — it is making your app describe itself to the agent.
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Your prompt won't fix AI-sounding writing. A lint rule will.
A prompt is a request that drifts. A lint rule is a gate that can fail. How I stopped trying to fix AI-sounding prose with longer prompts and put a deterministic linter on the publish path instead.
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Six checks that catch AI-sounding text before you publish
A platform flagged seventeen spots in my first article. Not one of them was about the content. Here are the six habits I now check before publishing, and the four I automated.