What AI agents are, and which one you need
A chatbot answers; an agent acts. What separates the two, what a production agent is actually made of, and how to tell which workflow in your business is the right one to start with.
Everything we've written on this
What is MCP? The Model Context Protocol explained for business and technical buyers
MCP is an open standard that connects AI apps to tools and data through one protocol. How it works, the security risks, and when an API is the better choice.
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Invoice processing automation: the AP workflow step by step, with the controls that keep it safe
Invoice processing automation explained: capture, extraction, 2- and 3-way matching, approvals, ERP posting, where AI fits, the controls and a checklist.
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AI in manufacturing: eight use cases, the data each needs, and how realistic they are
AI in manufacturing, function by function: quality, maintenance, planning, supply, quoting, documents and ERP. The data each needs, sourced figures, a verdict.
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AI in logistics: where it pays off, and what it needs first
AI in logistics pays off on email quotes, order entry, documents, tracking updates and exceptions. Sourced figures, what it needs first, and a decision table.
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n8n templates for business: which to start from, and what to fix before production
n8n lists more than 13,000 workflow templates. Which kinds suit a business, how to import one safely, and the eight fixes each one needs for production.
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What are agentic workflows? A plain definition, six patterns and when to use one
Agentic workflows explained: workflow vs agent, six patterns from prompt chaining to tool-using agents, with business examples and when to skip them.
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AI agent use cases: 20 jobs agents can do, rated for how well they work
AI agent use cases by function: 20 jobs from support to finance, each with the data it touches, the human checkpoint, the metric and a realism rating.
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AI agent security framework: the 3Cs and beyond
Docker's 3Cs (contain, curate, control) checked against OWASP, Google, Meta, NIST and real agent incidents, plus the gaps you still have to close yourself.
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When a multi-step AI agent fails in production
A multi-step AI agent at 95% per step is about 66% end to end after eight steps. Split or gate it. Book the $25,000, two-week AI Transformation Sprint.
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Patient intake agents + EHR: the pattern that works
AI patient intake EHR integration that cuts admin time when eligibility, consent, and chart write are one path. Book the $25,000 AI Transformation Sprint.
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Where AI agents pay off first in ecommerce ops
AI agents for ecommerce operations pay back first on returns, inventory, and exceptions — with published ROI. Book the $25,000 AI Transformation Sprint.
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Rules-based automation vs. AI agents: when you need the upgrade
Your Zapier flows and RPA bots aren't obsolete: they're cheaper and more predictable than an agent for the work they fit. The line is your exception rate.
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When should an AI agent stop and ask a human?
An AI agent should stop when an action is hard to undo, a mistake would go unnoticed, or it isn't confident. Four supervision patterns, and how to choose.
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Five signs your business is ready for an AI agent
Readiness isn't enthusiasm, it's five specific conditions. The checklist we run before we agree to build, and what to do if you only meet three of them.
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AI agent vs. chatbot: what's the difference (and which do you need)?
A chatbot answers; an agent acts. The real distinction — autonomy, tools, and multi-step work — and how to tell which one your workflow actually needs.
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HIPAA compliant AI: what it takes, which vendors sign a BAA, and what to check
HIPAA compliant AI needs a BAA at every hop touching PHI, minimum necessary access, audit logs and human review. Which AI vendors sign BAAs, and a checklist.
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AI support automation for SaaS: the playbook behind 60%+ autonomous resolution
How AI support automation SaaS hits majority autonomous resolution without torching CSAT: tier design, escalation, and evals, with real numbers.
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The production AI agent stack: what we actually deploy
The six layers a production AI agent stack needs: model, orchestration, retrieval, evals, observability, guardrails, and where teams cut corners.
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Want this running in your stack?
If this is the kind of work you're trying to get into production, a 30-minute call with an engineer is the fastest path to a scoped plan with a price.