What Over How: From Process Design to Goal Definition with AI Agents
Agents on the Scene
AI agents are rapidly transitioning from theory to practical use, offering significant potential across industries. Their rise promises a huge shift in productivity. We're not just talking about individual “digital workers”, but interconnected teams of AI, collaborating to tackle complex tasks. These are sometimes referred to as AI agent “swarms”, but that’s a term that suggests to me a kind of chaos that we certainly don’t expect or want, so I’ll avoid the term.
It's easy to get bogged down in the "how" – meticulously designing processes for agents to execute. We imagine drawing intricate flowcharts for this new, dynamic digital workforce. But this focus, while seemingly practical, might be missing the forest for the trees.
I would be remiss if I did not point out that this powerful technology necessitates robust governance and guardrails to ensure safe, secure, and ethical operation. Without careful proactive measures, their transformative power could lead to a whole range of negative outcomes.
Agents for Getting Things Done
If AI agents are really going to become independent, their power won't just be in following the processes that we design. It will be in their collective ability to determine the best process, or dynamically create new ones, to achieve a given objective. They'll analyze, adapt, delegate, and optimize in ways we might not even conceive of. Therefore, our critical task is fundamental goal definition, not just process design. In other words, focus on clearly defining goals for AI agents rather than meticulously designing their processes.
Instead of asking, "How should AI agents do this?”, we need to ask, "What is the core outcome we want to achieve?" Clearly articulating our goals is (and always has been) essential, as it minimizes ambiguity and allows for measurable progress, a principle that becomes even more critical when delegating to autonomous AI systems. If the goal is well-defined, an advanced AI system will be capable of devising, testing, and implementing the most efficient path to get there.
Focusing too intently on the "how" risks limiting the potential of these systems. We might inadvertently constrain them to outdated, suboptimal, or ultimately ineffectual methods simply because that's the process we initially designed.
Agents for Getting the Right Things Done
Furthermore, this new era of AI won't just be about execution. AI itself will become a crucial partner in defining and refining our goals. As agents begin working towards our initial objectives, they will collect new information, uncover new insights, identify opportunities, and highlight problems in our original thinking. This iterative feedback loop means AI will help us continually improve our understanding of what we should be aiming for, especially as we get closer to milestones and as conditions change, allowing for dynamic goal refinement based on real-time data and emergent patterns detected by the AI.
The real leverage for AI agents lies in their strategic thinking and input to shape our objectives, and a willingness to co-evolve those objectives with AI. Enlist support for goal definition to AI agents to unlock their full potential. What do we truly want to accomplish, and how can AI help us clarify and even redefine that as we progress? Once that evolving understanding is clear, we can empower these intelligent systems to find the most effective route, unlocking unprecedented levels of productivity and innovation.