Leading in the agentic era: How to put people first as you adopt AI agents

Google Cloud Consulting reports a shift from generative AI tools to agentic systems that execute multi-step tasks autonomously. Traditional workflows, structured like relay races with sequential handoffs, can create bottlenecks when agents are layered onto existing processes. The company advises moving from human-in-the-loop validation to a human-in-the-lead model, where agents perform the work while people set direction and own outcomes. This approach allows agents to handle parallel dependencies simultaneously, such as checking product availability, credit limits, and contract paperwork at once, reducing delays caused by multi-person queues.

Trust remains the primary barrier to adoption, with employees concerned about job security and accountability when agents handle routine tasks. Google recommends treating agents as digital coworkers by actively delegating tasks, defining their scope, and checking their work for accuracy. Clear boundaries should be set around decision-making authority, specifying which choices agents can make independently and which require human approval. Starting with low-stakes use cases, such as scheduling internal meetings, helps build familiarity before tackling high-stakes operations like financial forecasting.

Collaboration across teams is essential to identify where agentic capabilities fit within workflows. Cross-functional perspectives reveal end-to-end process gaps and help prioritize ideas based on technical feasibility and business impact. Top performers in hybrid teams are those who guide agents effectively, so rewarding team members for auditing an agent’s reasoning and catching logical drift reinforces desired behaviors. Coaching teams to understand how agents solve problems enables them to spend more time on creative work and decision-making.

Google Cloud Consulting advocates for a hub-and-spoke model to scale agent adoption without stifling innovation. A central AI council sets guidelines, maintains ethical guardrails, and manages a shared library of approved tools, preventing redundant work. Individual teams adapt these tools to their needs, experiment, and provide feedback. Internally, Google uses an Agent Catalog to share low-code agents and skills, but companies can implement similar systems through simple internal web apps to filter and sort employee-built agents.