Your agent created a double agent.
You’re sitting in the board meeting, and the numbers look good.
The mission was completed. In fact, it was completed with the help of AI agents, and your agents created other agents to work alongside them. A whole team of virtual agents pulled the pieces together, with your direction, of course. The work is done and the numbers tell a compelling story.
You’re proud of what you accomplished and how you did it.
Suddenly, you hear someone jump in and ask about the numbers; you look up at the dashboard and notice the errors. You panic; do you have a rogue agent? How did this happen?
The agent was doing exactly what you built it to do, on bad data, with no training to catch that bad data.
When building the dashboard, someone asked the agent for last quarter's marketing spend. The agent reached into two systems, pulled two different numbers, and because it wasn’t trained to flag a conflict, it split the difference and quietly invented a third number. That number made it to this board meeting looking crisp and certain with your name on it.
Two things can put a real human agent in danger in the field: bad intel, and no training to handle it. The same applies to AI agents: bad data, and agents with no training on how to handle it.
A spy working from bad intelligence can make the wrong decision at exactly the wrong moment, with consequences that can extend far beyond the mission. Historically, bad intelligence comes from double agents feeding fabrications to mislead their handlers.
An AI agent doesn't need a secret radio or a hidden allegiance to become a double agent. All it needs is bad intelligence with no direction on what to do with it.
The intelligence problem
The data glitch isn’t rare.
- 3 in 4 organizations now stand up a dedicated AI governance body, but only 12% call it mature (Cisco 2026 Data and Privacy Benchmark Study).
- Gartner predicts 60% of AI projects unsupported by AI-ready data will be abandoned (Gartner, 2025).
We've all heard "garbage in, garbage out" when it comes to CRM, asset management, ERP, PIMs, and the rest. So how do you turn messy data into actionable insights? And how do those insights help sharpen your agent in the field instead of creating confusion or sending it in with errors?
It all comes down to how your data is governed. Build an agent on bad data and it scales it, quietly, over and over, faster than any human could. That should scare you.
AI-ready data is clean, connected, and governed with shared definitions, traceable lineage, and clear ownership. It's the difference between a verified dossier and a rumor scrawled on a napkin.
But governing the data is only half the operation. The other half is the agent you send in to act on it.
The agent you never trained
You don't hand a recruit a badge and drop them behind enemy lines on day one. There's a spy academy with training, rehearsal, a dossier of everything that can go wrong, and a supervisor who signs off before deployment.
For AI agents, that academy is the Software Development Lifecycle (SDLC), or the Agentic SDLC when the agents run it themselves. In the Agentic SDLC, agents help build, test, and version other agents, or work alongside your developers to create the features, applications and tools that are critical to your business. But if humans don't build the right Agentic SDLC first, the agents never learn to question the intel.
Your SDLC is the handler. It sets the mission plan every agent follows, even the agents that build other agents. Create rogue agentic SDLC programs and the board gets wrong numbers, with your name on them.
What the fix looks like on Google Cloud
Google built the answer into the platform, one layer for each half of the problem:
The intel.
BigQuery is your single source of truth; Knowledge Catalog sits on top as a shared semantic layer. Define "spend" once, govern it there, and every human and every agent reads the same number.
The handler.
Build the agent with Google's Agent Development Kit (ADK), run it through Gen AI evaluation before it goes live, then deploy on a managed runtime with Cloud Observability, so you can see what it did and catch it going rogue before anyone acts on the output.
SoftServe is a Premier Google Cloud Services Partner. We help you govern the data and build the agents that run on it. Check out our Data Governance Pilot. It's a four-week program to build a governed, AI-ready foundation on Google Cloud.
