Management Methods for AI Agents: OODA, GTD, PDCA, TOC, First Principles, OKR
Apply OODA, GTD, PDCA, TOC, first principles and OKRs to agent workflows. Inspect goals, state, decisions, model limits and review gates.
If an agent loops, drops a constraint or reports success on a failed run, inspect the decision and review process alongside model and tool limits. Management methods give you checks to apply; they do not identify every failure cause.
OODA means observe, orient, decide, act. GTD (Getting Things Done) externalises pending work; PDCA means plan, do, check, act. TOC is the Theory of Constraints. SMART describes specific, measurable, achievable, relevant and time-bound goals; OKRs pair objectives with key results.
Eight modules in the player, from OODA to a small operating system. Pair it with Harness Engineering when the failure is the environment, not the cadence.
For whom
- Engineers who already run LLM agents and watch them lose the thread on long chains
- Leads who need a cadence a team can keep, not a personality lecture
- Not MBA time-management, not a ChatGPT intro, not a promise that a framework replaces the team
Modules
- The Agent as Manager
- OODA loop
- GTD
- PDCA
- First Principles
- Theory of Constraints
- SMART / OKR
- Synthesis: Agent OS
After the course
- See the agent as a self-managing system: goal, decision loop, prioritization
- Build a fast OODA loop and treat Orient as the main lever
- Unload working memory into external memory the GTD way
- Run a slow PDCA loop through a verification gate
- Decompose a task to axioms instead of cargo-cult prompts
- Find the constraint and refuse to optimize everything at once
- Turn a vague goal into a measurable spec
- Assemble two loops — fast OODA and slow PDCA — into an operating layer