I ran two sessions this week with AI champions at a customer I'm working with. The topic: what agents actually mean, and what the role of humans becomes when working with them.
The "elephant in the room" question was - "If agents can do things autonomously, what's left for us?"
Everything, it turns out. Just different work.
2025 was the year of AI agents. Single assistants that could handle tasks autonomously - summarizing documents, drafting emails, answering customer questions. Useful, but limited. A lot of it also focused on AI Agents for coding and programming.
2026 is set to be the year those agents learn to work together. Instead of one all-purpose agent trying to do everything, organizations are deploying teams of specialized agents: a researcher gathers information, a coder implements solutions, an analyst validates results. Each agent fine-tuned for specific capabilities. Each handing off to the next.
Gartner reported a 1,445% surge in multi-agent system inquiries between Q1 2024 and Q2 2025. By end of 2026, 40% of enterprise applications will embed AI agents - up from less than 5% in 2025.
The question isn't whether agent teams are coming. It's whether you're ready to lead them.
Here's the framework I shared in those sessions. When you work with AI agents - whether a single assistant or a coordinated team - your role breaks down into five areas:
Job - Define and refine the tasks and goals the agent should strive to achieve. Agents don't set their own objectives. You do. This means being precise about what success looks like, breaking complex goals into achievable tasks, and refining those definitions as you learn what works. The clearer your intent, the better the agent performs.
Context - Provide access to relevant context, tell it what is important and why. Agents only know what you give them access to. Your role is deciding what information matters, why certain details take priority, and how to frame the situation. This is judgment work - the kind of contextual understanding that humans excel at.
Autonomy - Decide how much autonomy to give, and under which conditions to increase or decrease it. This is the dial you control. Some tasks warrant full delegation. Others need checkpoints. The skill is knowing which is which - and adjusting as trust builds or circumstances change. Only 34% of organizations successfully implement agentic AI. The failures share a pattern: too much autonomy, too fast.
Tools - Give access to the right tools, with permissions and credentials to use them. Agents need capabilities to act. Your job is curating which tools they can access, setting appropriate permissions, and ensuring they have what they need without overexposing sensitive systems. Think of it as provisioning a new team member - thoughtful, not blanket access.
Monitor - Evaluate the actions, outputs, processes, evolution, and "drift." Agents can drift from their intended behavior over time. Your role is watching for that drift, assessing output quality, and intervening when needed. This isn't micromanagement - it's quality assurance and continuous improvement.
This week, take one task you're already doing with AI - document summarization, email drafting, research - and map it against the five responsibilities. Where are you being precise? Where are you leaving gaps? The framework isn't just for agent teams. It's for any human-AI collaboration.