For the past few months, I’ve been obsessed with a question: Can a personal AI agent truly think, plan, and act on my behalf – like a teammate, not a tool?
Not a chatbot. Not a prompt wrapper. I mean something with actual agency – something that perceives the world, reasons about it, makes decisions, and grows alongside me. As a solo founder, I’m done with productivity tools and their clunky UIs – I want a personal agent that understands my goals, evolves with me, and actually cares to see me win. That’s the dream. And I think I just got one step closer.The Search for True Autonomy
When you start building personal agents, you quickly realize how shallow most “AI autonomy” really is. Everything sounds autonomous – until you open the hood. I studied the leading architectures trying to emulate autonomy:- ReAct Agents – Reason + Act loops that think, execute a tool, then observe the result.
- Planning Agents – Agents that explicitly plan tasks and execute them step-by-step.
- Deep Agents – Hybrids combining reasoning, planning, tool use, and memory.
The ReAct Pattern: Smart, But Not Strategic
ReAct (Reason + Act) runs inside the model: Think → Act (pause to run a tool) → Observe → Think again. It’s elegant and reactive – but fundamentally short-sighted. ReAct agents make the best next move, not the right long-term move.Imagine you’re driving across a city using one rule: “At every intersection, take the road that looks fastest right now.” You’ll make locally optimal choices – and still end up stuck in traffic.That’s ReAct. Tactical brilliance, strategic blindness. The other issue? Generic reasoning. You can tell the model what to do (“analyze cash flow”) but not how to think. So you get answers that sound correct – but lack expert discipline. It’s the difference between asking a student and an accountant to prepare a report. One googles definitions. The other applies a structured process. ReAct can’t do that. It doesn’t think like an expert – it just responds like a chatbot.
Planning Agents: Order Without Intelligence
To fix that, the next wave introduced explicit planning: create a to-do list, then execute each step. Some follow a ReWOO style – build the plan once, then execute line by line. Others use Reflective Planning – re-evaluating the plan after every action. They’re organized, predictable, and modular – but they lack self-awareness. A planner can write a perfect to-do list, but when reality changes it has no idea how to adapt. They can’t notice that a plan is failing because there’s no reasoning layer watching over execution. They’re like good project managers who don’t know when to pivot.Deep Agents: The Closest We’ve Come
Then came Deep Agents – a more advanced architecture combining ReAct’s reasoning, planning modules, memory, and tool use. LangChain’s implementation uses a clever trick called theTodoListMiddleware: it tells the model, “You have a file called todos.md where you can manage your plan.” The agent then “edits” this markdown file as if it’s updating its own thoughts.
It’s clever – almost magical. But here’s the problem: the agent isn’t actually thinking about its plan. It’s just editing text. It doesn’t know it’s replanning. It doesn’t understand success or failure. And because the plan lives as text, it can’t store rich metadata like tool results or failure states. Eventually, the plan drifts out of sync with reality. That’s not autonomy. That’s an illusion of autonomy.
The Breakthrough: Thinking in Loops, Not Lines
After months of experiments, I realized: real autonomy doesn’t come from chaining patterns. It comes from building cognition. We needed an architecture that wasn’t just reactive or procedural – but reflective; something that could observe its world, orient itself within context, decide strategically, and act deliberately. That’s when I rediscovered a classic decision model called the OODA loop – Observe → Orient → Decide → Act – and realized it was the missing piece.Meet the OODA Agent: The Cognitive Core of a Personal Agent
The OODA Agent isn’t just another agent pattern. It’s a cognitive architecture – a structured brain for a personal agent. It treats planning, state, and reasoning as first-class citizens. Let’s break down how it works.1) Planning: Structured, Rich, and Explicit
Instead of pretending to edit a text file, the OODA Agent has a structured internal state called a working plan. Each plan step is a full object – not a sentence. It stores:- the step description,
- which tool to use,
- its arguments,
- status (pending, completed, failed),
- result or error message.
2) Execution: Atomic and Self-Correcting
Execution is handled by the Python orchestrator. When the agent saysexecute_tool, the orchestrator runs it and automatically updates the corresponding plan step with the result.
No forgetting. No manual updates. The plan and the world stay perfectly in sync. It’s like having a brain that not only acts, but remembers exactly what it did and what happened next.
3) Reasoning: Structured and Self-Aware
Here’s where the magic happens: the agent’s reasoning isn’t free-form text – it’s structured thought. Each “thought” has fields likesituation_assessment, complication_analysis, and strategic_decision. This forces the model to reason explicitly about context, challenges, and next actions – like a real strategist, not a text predictor. It doesn’t just think about the world. It thinks about its own thinking.
4) Tool Use & Model Independence
The OODA Agent doesn’t depend on the LLM’s proprietarytool_call syntax. It defines its own AgentAction schema – portable JSON that any model can output.
That means the architecture isn’t tied to a single provider. It’s model-independent, future-proof, and extensible. You can give it tools, sub-agents, or even new reasoning modes. It’s a real platform for cognition.
Seeing It in Action
Full implementation and test harness are on GitHub: github.com/moe1047/odoo-agent-example Below is the core loop that runs the OODA cycle – observe state, get a structured decision, act, and atomically update plan state. (Types, models, and mock tools are elided here for brevity.)def run_cognitive_agent(
initial_state: CognitiveAgentState,
planner_llm,
tool_registry: Dict[str, Callable],
max_steps: int = 10,
):
state = initial_state
for i in range(max_steps):
print(f"\n--- Step {i + 1}/{max_steps} ---")
print(f"Goal: {state.goal}")
print(f"Plan: {[s.step for s in state.working_plan]}")
# Observe → Orient → Decide
agent_response = get_agent_decision(state, planner_llm)
action = agent_response.action
state.history.append(
{"thought": agent_response.thought, "action": action.model_dump()}
)
print(f"Thought: {agent_response.thought}")
print(f"Action: {action.type}")
# Act
if action.type == "finish":
print("--- Agent Finished ---")
return {"final_answer": action.final_answer, "state": state}
elif action.type == "update_plan":
print("Updating plan.")
state.working_plan = action.new_plan
state.history.append({"observation": "Plan has been updated."})
elif action.type == "execute_tool":
tool_name = action.tool_name
tool_args = action.tool_args
print(f"Executing Tool: {tool_name} with args {tool_args}")
if tool_name not in tool_registry:
result = f"Error: Tool '{tool_name}' not found."
else:
try:
result = tool_registry[tool_name](**tool_args)
except Exception as e:
result = f"Error executing tool: {e}"
print(f"Tool Result: {result}")
state.history.append({"observation": result})
# Atomic: execution + plan state update
for step in state.working_plan:
if step.tool == tool_name and step.status == "pending":
step.status = "completed" if "Error" not in str(result) else "failed"
step.result = str(result)
break
print("--- Max steps reached ---")
return {"final_answer": "Max steps reached.", "state": state}Why This Matters
It’s about building real, personal agents – entities that can work alongside you, learn your context, and grow over time. By separating thinking (the model’s cognition) from doing (the orchestrator’s execution), and by giving both a structured world to reason about, we move from automation to agency. OODA Agents are the foundation for that next leap – for personal agents that can truly understand goals, handle complexity, and evolve with their user.
Right now, I’m using this architecture to build a Time Manager Personal Agent – my first practical implementation of true autonomy. Its goal is simple but transformative: replace my to-do list and calendar, and then go far beyond what a UI could ever achieve. Not just scheduling tasks, but understanding my priorities, reasoning about my goals, and deciding how my time should actually be spent. That’s the power of the OODA Agent loop – a mind that not only acts, but reflects.
It’s not just the future of personal agents. It’s the beginning of personal intelligence.
