AI agent operations
Runbooks, identity, approval gates and release checks for browser agents and tool-using AI workflows.
AI Insights
AI Marketing Social SEO Growth
AI
Short, practical thinking for teams building with AI, automation, marketing systems and search-driven growth.
Ideas worth turning into systems.
Social growth systems
Creative feedback loops, video memory and automation assets that connect content work to qualified demand.
AI search and reporting
Evidence-led SEO, reporting automation and resources that turn analytics into decisions.
Coding Agents Need Delivery Gates
Coding agents are moving from autocomplete into branches, pull requests, tests and asynchronous delivery. The teams that benefit will not be the ones that delegate everything. They will be the ones that define task scope, evidence, review and release gates before generated code touches production.
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Browser Agents Need Runbooks
Browser automation agents can click, type, inspect screens and move through real web apps. That makes them useful, but also operationally risky. The difference between a demo and a production workflow is a runbook: permissions, recovery rules, traces, screenshots and human approval for actions that can affect customers, money or public accounts.
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Agent Handoffs Need Contracts
Multi-agent systems do not fail only because the model gives a weak answer. They fail when responsibility moves between agents without a contract: no owner, no reason, no state, no trace and no rule for when a human must step in.
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Agent Evaluations Beat Demos
Client-facing agents don’t fail because the model is “bad” — they fail because teams ship a demo without a repeatable evaluation loop. If you can’t measure success, safety, and tool behavior across real scenarios, you’re not deploying automation. You’re deploying uncertainty.
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AI Agent Identity Checklist Before Tool Access
Before an AI agent reads files, sends messages or changes systems, check its service identity, least-privilege scopes, revocation path, approval gates and audit trail.
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MCP Integrations Need Sandboxes
Model Context Protocol is making agent integrations ridiculously easy — and that’s exactly why agencies need sandboxing, allowlists, and audit trails before “one connector” becomes an incident.
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Sandboxed Agents Need Governance
In 2026 the hardest part of "AI agents" isn’t the model — it’s the guardrails: what the agent can touch, how work is reviewed, and how you prove it behaved safely when it operates on real systems.
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Agency AI Delivery Systems
AI becomes commercially useful for agencies only when it stops being a clever prompt and becomes a delivery system: connected data, review rules, client-ready output and a workflow the team can repeat.
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