OKRs for Artificial Intelligence
In business, agents face the same challenges as humans. For decades, corporations have worked on one important task — transforming unpredictable, sometimes chaotic humans into predictable performers who deliver stable results. All company management was built around this: methodologies, processes, report templates, approval chains, and organizational structures — all crutches created to manage stochastic systems (people) and achieve consistent outcomes. This often resulted in tedious and ineffective routine corrections aimed at patching holes in the system. Without this, a company quickly turns into a herd without a leader.
Now, the situation is similar with AI agents — their models based on large language models are inherently stochastic and probabilistic by nature. To overcome this, we implement orchestration mechanisms, checks, self-assessment, limiting frameworks, templates, and structured data formats — JSON output. We are essentially creating additional layers around this stochastic system to increase its predictability.
A nearly exact correspondence can be drawn:
– OKRs = setting agent goals
– Stand-ups = regular status updates and checkpoints
– Regulations = prompt templates and algorithmic instructions (runbooks)
– Peer review = self-checks and mutual reviews between agents
– Organizational structure = process orchestration graph
– Performance review = effectiveness benchmarking
At the same time, solutions for managing humans and systems differ significantly. Humans need motivation, context, and culture — factors of human nature. For agents, the focus is on detailed validity of results, retries for error correction, and structured memory. Each has its vulnerabilities: humans tend to cut corners or avoid responsibility, while agents may hallucinate or provide incorrect answers. Humans tire out and lose focus; agents do not tire physically but can lose context due to limitations in memory window length.
If you are working on building agent-based systems, it’s useful to study approaches to human management within organizations. Management as a science offers a rich set of practices for orchestrating such systems. Agents are clearly not humans, but tasks like “making cat herding predictable” require proven solutions and patterns developed over many years.
Overall, the idea is that principles of work organization and control applied to humans can provide valuable insights when constructing complex autonomous systems. Achieving stability and predictability is not an easy task even for humans; therefore, it is important to apply proven management methods to oversee these digital “teams.”
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