Latest from Fluency

Agentic automation in advertising works in two steps: an AI agent proposes an action, and a deterministic agent decides whether it goes live. Learn how it can scale your AdOps.

Managing 100+ client accounts? Here are the criteria for evaluating Google Ads automation and agentic tools, what to prioritize, and what to avoid.

Eight agentic advertising use cases for agencies and brand teams across four workflows: campaign launches, budget pacing, creative management, and automated reporting.

Here’s what it costs to build an agentic advertising system in-house and what to look for if you're buying. A decision framework for advertising teams.

AdCP and AAMP are the emerging protocol standards for AI advertising. Here's what each does, how they differ, and what they still don't solve.

Agentic advertising uses AI agents and governed automation to run campaigns autonomously. Here's what it actually means and how it's built.

Integrating an AI agent directly to ad platform APIs is risky. Here's how a deterministic execution layer keeps agentic advertising safe and predictable.

Meta’s Opportunity Score is a powerful health check, but how important is a 100 score? Learn how to balance Meta algorithmic health with your advertising strategies.

Safely scale your agentic advertising strategy. Follow this 4-step framework for unified data, deterministic execution, and reliable AI execution.

Mike Lane, Fluency’s co-founder and CEO, gives a firsthand analysis of Google’s new AI infrastructure. See what it means for your ad agency's data feeds and operational capacity.

Scale multi-location strategies with agentic automation. Learn how two enterprises use operational governance to run complex multi-location campaigns easily.

Discover the core best practices for enterprise Google Ads management. Master automated testing, compliance, and multi-location account scaling.