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Which Job Should You Agentify First?

Most AI agent pilots fail on the first pick, not the technology. A four-gate decision tree to choose which job to agentify first: volume, repeatability, error tolerance, escalation cost.

From TEAMCAL AI to ANCI: A Fresh Look for a Bigger Mission

TEAMCAL AI is becoming ANCI. Same team, same product, same trust. A name built for what scheduling looks like in the agent era.

Agent at Work: *The Taxonomy*

Every vendor now claims to ship an agent. The taxonomy that cuts through the noise: chatbot, workflow automation, RPA, copilot, and agent — sorted by six building blocks every automation shares, with the one architectural test that tells them apart.

Introducing ANCI: The Agent Infrastructure for Scheduling

Today we are introducing ANCI, the agent infrastructure for scheduling. Built on six years of scheduling agents, ANCI is how we hire scheduling work to named agents instead of buying more software. Meet ANCI AI, Ray, and the rest of the family.

Why AI Agent Security Is the Defining Risk of Enterprise AI

The conversation has moved from what these systems can do to what happens when they act inside the business. The answer is an architecture problem, not a prompting problem.

The Goldilocks Window: Why Vertical SaaS Has the Right to Win the Agent Era

For the next two to four years, AI agents will not compete against other agents. They will compete against labor. That is the Goldilocks Window — and it will not stay open.

The SaaSpocalypse: How Agentic AI Is Rewriting Your Company, Your Fundraise, and Your Exit

John Eng opened the founders dinner with that word, then spent forty minutes explaining why he meant it literally. The frame matters. So does what comes after.

The Agentic AI Revenue Stack

How founders operate the 4 phases of sales in the age of AI agents. A recap from the Right Side Capital Sales Bootcamp.

Dispatch from 2031: a day in the life of an AI agent.

Not Know Whether 'Feel' Is the Correct Verb A Day in the Personal Log of Ray-7.3.1, Recruiting Agent PERSONAL LOG Agent ID: RAY-7.3.1, Recruiting Cycle: 4,287 Local stardate: Tuesday, March 4, 2031

When Agents Break

The growing shift in tech reframes traditional software as “AI agents,” promising systems that act rather than simply respond, but in reality these agents operate within guided, probabilistic boundaries rather than true autonomy. Today’s AI agents fail differently from traditional software—not with clear crashes, but through ambiguous, often confident decisions that can be subtly wrong and difficult to trace. This tension, along with the idea that many agents are essentially rebranded SaaS, emphasizes the need to design for visible, controllable failure rather than assuming reliability.