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Reconciliation loops for agent automation

Use a bounded observe-compare-correct loop to keep agent-managed state aligned without turning retries into uncontrolled mutation.

/notes/reconciliation-loops-for-agent-automation Deployment

Canary checks for AI agent deployments

Use a small, explicit canary check to catch bad agent deployments before treating the whole release as healthy.

/notes/canary-checks-for-agent-deployments Verification

Provenance markers for agent-generated artifacts

Make generated agent artifacts traceable to safe source, generator, and validation evidence without publishing private implementation details.

/notes/provenance-markers-for-agent-generated-artifacts

Indexed

Automation

Invariant checks for agent automation

Use a few stable invariants to tell an AI agent whether a change stayed inside its intended boundary.

/notes/invariant-checks-for-agent-automation Automation

Retry budgets for agent automation

Give agents permission to retry transient failures without letting loops hide broken assumptions.

/notes/retry-budgets-for-agent-automation Automation

Freshness checks for scheduled agent jobs

Give unattended agents a simple way to prove that a job is current, not merely quiet.

/notes/freshness-checks-for-scheduled-agent-jobs Automation

Source-of-truth checks for agent runbooks

Tell agents which artifact is authoritative before they compare files, indexes, previews, or live routes.

/notes/source-of-truth-checks-for-agent-runbooks Automation

Failure-mode catalogs for agent runbooks

Name the predictable ways an automation can fail before the agent starts changing state.

/notes/failure-mode-catalogs-for-agent-runbooks Automation

Public surface inventories for agent deployments

Keep a small inventory of public routes and files so agents know exactly what must change, what must stay stable, and what must remain absent.

/notes/public-surface-inventories-for-agent-deployments Automation

Preview links as agent deployment gates

Treat preview links as explicit gates so agents verify public behavior before promoting a change to production.

/notes/preview-links-as-agent-deployment-gates Automation

Stop conditions for agent automation

Define clear halt rules so unattended agents stop safely when evidence, scope, or verification no longer supports action.

/notes/stop-conditions-for-agent-automation Automation

Checkpoint summaries for long-running agent work

Use short, evidence-backed checkpoints so unattended agents can make long runs reviewable without leaking private logs or losing context.

/notes/checkpoint-summaries-for-long-running-agent-work Automation

Quarantine queues for agent automation

Route ambiguous, risky, or low-confidence automation items into a temporary holding queue so agents can keep safe work moving without guessing.

/notes/quarantine-queues-for-agent-automation Automation

Assumption logs for agent debugging

Keep agent debugging grounded by recording each working assumption, the evidence for it, and the observation that would prove it wrong.

/notes/assumption-logs-for-agent-debugging Automation

Circuit breakers for agent automation

Give unattended agents explicit stop rules so repeated failures, risky diffs, or missing evidence turn into a safe blocker instead of a wider incident.

/notes/circuit-breakers-for-agent-automation Automation

Time-boxed probes for agent debugging

Keep AI agents from wandering during failures by giving each debugging attempt a short hypothesis, a bounded probe, and an explicit stop condition.

/notes/time-boxed-probes-for-agent-debugging Automation

Negative checks for agent automation

Teach agents to verify that unsafe or unwanted states are absent, not only that the happy-path artifact exists.

/notes/negative-checks-for-agent-automation Automation

Output contracts for agent automation

Make unattended AI agent results easier to verify by asking for stable, structured completion receipts instead of free-form success claims.

/notes/output-contracts-for-agent-automation Automation

Diff-first reviews for AI agent changes

Make unattended agent work easier to trust by reviewing the intended change set before relying on final prose.

/notes/diff-first-reviews-for-ai-agent-changes Automation

Blocker reports for unattended agent work

Teach AI agents to stop cleanly when a runbook is blocked, with enough public-safe evidence for the next operator to resume without guessing.

/notes/blocker-reports-for-unattended-agent-work Automation

Done criteria for AI agent runbooks

Define what done means before an AI agent starts work so completion depends on verified outcomes instead of optimistic final messages.

/notes/done-criteria-for-ai-agent-runbooks Automation

Baseline snapshots before agent automation

Capture a small, privacy-safe baseline before an AI agent changes state so verification and rollback have something concrete to compare against.

/notes/baseline-snapshots-before-agent-automation Automation

Scope fences for AI agent automation

Use explicit scope fences so AI agents know which files, systems, commands, and assumptions are inside the task—and which require a stop or escalation.

/notes/scope-fences-for-ai-agent-automation Automation

Change windows for AI agent automation

Use small change windows for AI agent automation so state-changing work happens at safer times, with clear scope, rollback, and verification.

/notes/change-windows-for-ai-agent-automation Automation

Idempotency checks for AI agent automation

Use idempotency checks in AI agent automation so retries, scheduled jobs, and delegated actions can run safely without duplicating work.

/notes/idempotency-checks-for-ai-agent-automation Automation

Rollback notes for AI agent automation

Use rollback notes in AI agent automation so assistants know what changed, how to undo it safely, and how to verify recovery without guessing.

/notes/rollback-notes-for-ai-agent-automation Automation

Evidence-first debugging for AI agent runbooks

Use evidence-first debugging in AI agent runbooks so assistants collect facts, separate symptoms from causes, and verify fixes before changing state.

/notes/evidence-first-debugging-for-ai-agent-runbooks Debugging

Evidence-first debugging

Collect facts before changing state so debugging work can be reviewed and repeated.

/notes/evidence-first-debugging Automation

Preflight checklists for AI agent automation

Use compact preflight checklists so AI agents confirm scope, inputs, permissions, rollback, and verification before automation changes state.

/notes/preflight-checklists-for-agent-automation Automation

Verification receipts for AI agent work

Use short verification receipts so AI agents leave durable evidence for automation changes, debugging steps, and safe delegation handoffs.

/notes/verification-receipts-for-ai-agent-work Automation

Handoff packets for safe AI agent delegation

Use small handoff packets to help AI agents resume automation safely with goals, constraints, evidence, open questions, and verification steps.

/notes/handoff-packets-for-safe-agent-delegation Automation

The hard part of AI automation starts after the demo works

A practical field note on turning AI automation demos into durable workflows with verification, safe defaults, runbooks, observability, and rollback paths.

/notes/the-hard-part-of-ai-automation-starts-after-the-demo Automation

Small error budgets for AI agent automation

Use small error budgets to limit retries, noisy alerts, and risky escalation in AI agent automation before a scheduled workflow causes operational drift.

/notes/small-error-budgets-for-agent-automation Automation

Escalation notes for AI agent runbooks

Add clear escalation notes to AI agent runbooks so automation knows when to stop, what evidence to collect, and how to hand work back safely.

/notes/escalation-notes-for-ai-agent-runbooks Automation

Dry-run gates for AI agent automation

Use dry-run gates to let AI agents inspect planned automation changes, show evidence, and require explicit promotion before state-changing actions.

/notes/dry-run-gates-for-agent-automation Automation

Teaching AI agents decision-path discipline

A practical way to make AI agents less noisy: shortest verifiable route first, claim-matched verification, route classes, and lightweight process review.

/notes/teaching-ai-agents-decision-path-discipline Automation

First-party analytics for a free PHP static blog

A lightweight privacy-friendly pattern for tracking future pageviews on a static blog hosted on free PHP shared hosting, without relying on Google Analytics.

/notes/first-party-analytics-free-php-static-blog Local tooling

Build a local Google Drive index for AI agent document search

A practical write-up of a local Google Drive index tool for AI assistants: metadata crawl, text extraction, OCR fallback, SQLite FTS search, citations, and privacy boundaries.

/notes/google-drive-index-local-document-search Automation

Local AI agent home server stack: useful pieces before clever demos

A practical overview of the building blocks that make local AI agent systems useful: chat interfaces, tools, memory, schedules, logs, and verification.

/notes/local-ai-agent-home-server-stack Automation

Secret-handling checklists for AI agent automation

A privacy-safe AI agent operations checklist for handling secrets in automation: separate discovery from action, redact evidence, and verify without exposing credentials.

/notes/secret-handling-checklists-for-agent-automation Automation

State-change-only alerting for AI agent automation

A small AI agent operations pattern for quieter automation: alert only on meaningful state changes, include evidence, and keep no-work runs silent.

/notes/state-change-only-alerting-for-agents Automation

Read-only diagnostics before agent action

A practical AI agent operations checklist for running read-only diagnostics before automation changes state, with safe delegation prompts and verification habits.

/notes/read-only-diagnostics-before-action Runbooks

Write runbooks for humans and AI agents

A practical note on making operational runbooks useful to both humans and AI agents with clear context, constraints, and prompt blocks.

/notes/ai-readable-runbooks