AI, Agents and Outcomes
date
Nov 11, 2025
slug
ai-agents-outcomes
status
Published
tags
Principle
AI
summary
From Process to Outcomes.
The shift happening now is clear: we’re moving from hiring for process to hiring for results. AI accelerates this transformation through what Karpathy calls the autonomy dial, your ability to set how independent AI agents are in driving real business impact.
type
Post
Here's a surprisingly misunderstood truth I've seen again and again: outcomes matter way more than enablement.
For example, I've watched teams spend months building "comprehensive testing enablement platforms". Beautiful, horizontal, cross-platform solutions with detailed timelines and impressive technicals. But none of that matters unless it delivers concrete outcomes: more tests implemented, bugs caught earlier, improved code quality. Meaning what will drive the bottom line for the business is engineers actually using those platforms and catching bugs.
Technology untethered to business outcomes is like cherry without the cake. The most successful operators focus on driving existing top line metrics with measurable impact rather than simply adding new tools for the sake of it. Or put another way, they focus on impact.
This is the shift from hiring for process to hiring for outcomes. Just like you hire an engineer to deliver customer value, not to follow a specific coding methodology or implement the latest architectures patterns.
The Autonomy Dial: Your New Superpower
Agentic workflows represent AI-driven processes where autonomous agents make decisions, take actions, and coordinate tasks with minimal human intervention, adapting to real-time data and unexpected conditions.
The autonomy dial is a mental model for the future of work. Instead of micromanaging AI tools, you'll set the level of independence appropriate for each task. For example, applied to coding:
- Dial at 20%: AI suggests improvements to your existing code
- Dial at 60%: AI writes entire functions based on your specifications
- Dial at 90%: AI agents autonomously implement features from high-level requirements
Modern agentic AI systems are structured around four components: Perception (data collection), Decision-Making (algorithmic guidance), Learning (continuous improvement), and Action (real-world execution).
Notice 90% not 100%. For now, we’ll always need humans in the loop (at least until AGI, but that’s another topic).
What This Means for You
The agentic revolution isn't coming, it's here. 93% of IT executives are highly interested in agentic AI for business, with the global AI market projected to reach $267 billion by 2027.
We're at the cusp of autonomous outcomes, where AI agents will work at the layer of results rather than processes. The companies that understand this shift, that focus on outcomes, that embrace the autonomy dial, that treat LLMs as infrastructure rather than novelty, those are the ones that will thrive.
The future isn't about replacing humans with AI. It's about humans working with AI agents to achieve outcomes that were previously impossible. The question isn't whether this will happen, but how quickly you'll adapt your mental models to this new reality.