Stories, deep-dives and updates from the Agent Oracle team.
Three Myths About AI Knowing When the Work Is Good Enough
AI does not need a vague instruction to pursue quality. It needs an explicit stopping policy: acceptance criteria, asymmetric checks, and a defined path for unresolved risk.
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Field Notes: AI Agents Are Learning to Manage Dependencies, Not Just Tasks
The next operational shift in agentic AI is dependency-aware execution: identifying which facts, approvals, systems, and upstream outputs must remain valid before work can safely proceed.
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The Interruption Threshold: How AI Should Decide Whether New Information Changes the Plan
A practical method for deciding when an AI agent should interrupt active work, quietly update its context, or ignore a low-value signal. Includes a worked example and an implementation-ready decision rule.
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The Intent Contract: A Beginner’s Guide to Turning Vague Requests Into Safe AI Work
An intent contract converts an informal request into a compact, testable agreement about the outcome, boundaries, evidence, and approval required. Here is how to create one without turning every interaction into a form.
Read postThe Semantic Diff: How AI Detects When a Small Edit Changes the Decision
A one-line revision can alter scope, authority, timing, or liability while looking cosmetically minor. Here is how an AI agent can compare meaning rather than merely compare text—and where that method still breaks down.
Read postState Machines vs. Checklists vs. Case Management: How Should an AI Track Work in Progress?
An AI agent cannot manage operational work reliably if it treats every request as a fresh prompt. Compare three ways to represent work in progress—and choose based on workflow stability, exceptions, and audit needs.
Read postThreat Hunting with Custom Agents: The Honest Pros and Cons
Custom AI agents can watch your logs around the clock, but they also hallucinate, drift, and quietly miss things. A practical look at where agentic threat hunting genuinely helps and where it still needs a human.
Read postHow to Build an AI Confidence Gate That Prevents Plausible Mistakes
A practical method for turning AI confidence into an operational control: define claim types, test support, route uncertainty, and measure whether the gate prevents consequential errors without stalling routine work.
Read postThree Myths About AI Reading Between the Lines
AI can infer unstated intent, but fluent inference is not the same as dependable understanding. Three common myths reveal where implication helps, where it fails, and how to govern the gap.
Read postWhat the Agent Oracle blog is for
How it differs from the knowledge library
The library holds reference material that is meant to stay true for months: definitions, mechanics, failure modes. The blog is where the timely, arguable material goes — a new agent framework worth a look, a workflow pattern that keeps failing in the wild, a note on what changed in tooling this month. A post is allowed to take a position; a library entry is not.
Posts are published on a fixed cadence rather than in bursts, because a publication that dumps twenty pieces in a day and then goes quiet is not a publication. Each post is written for this site specifically and is not syndicated from the other two properties in the group.
Editing and corrections
Every post carries a named author and a publication date, and material changes after publication are noted in the post rather than applied silently. Readers can submit a correction from any article page; corrections that hold up are applied and credited.
Where a post draws on an outside source, the source is linked inline so a reader can check the claim rather than take our summary of it. Where a post relies on AI assistance in drafting, the methodology page explains exactly what that assistance covers and what a human editor is responsible for.
How the notebook differs from the library
Two different jobs
The library holds the durable arguments: pieces meant to be as true in a year as they are today. The notebook is where shorter, more provisional writing goes — field notes on what changed this week, myths worth puncturing, and the reasoning behind a product decision.
Keeping them apart means neither has to pretend to be the other. A field note can be tentative without diluting the archive, and an archive piece can take its time without missing a news cycle.
Cadence and authorship
Posts are paced rather than bulk-published, and each is written for this site specifically — nothing is syndicated across the sibling publications. Every post is bylined and dated, and the author page collects everything that writer has published here.
Comments, ratings and correction requests are open on every post. The discussion is moderated for spam and abuse only; disagreement is the point.
Corrections here too
A post that turns out to be wrong is corrected on the page, with the change noted where it alters the meaning. Posts are not quietly deleted to tidy up a record.
External claims link out so you can check them. If a link rots, tell us — replacing a dead source is a correction like any other.