Agent Planning: The Architecture That Changes Everything
by Zane White, Founder
Beyond the Single Prompt
For years, the dominant paradigm in AI interaction was simple: prompt in, response out. Ask a question, get an answer. Request a task, receive a result. This single-shot model worked well enough for simple tasks, but it fundamentally misunderstood how intelligence actually operates.
Real intelligence doesn't solve problems in one step. It plans, it iterates, it reconsiders. It breaks complex challenges into manageable pieces and tackles them systematically. This year, we've finally built AI systems that work the same way.
Welcome to the age of agent planning.
What is Agent Planning?
At its core, agent planning is the capability for an AI system to:
- Decompose a complex goal into discrete subtasks
- Sequence those subtasks in logical order
- Execute each step while maintaining context
- Adapt the plan based on intermediate results
- Synthesize the outputs into a coherent final result
This sounds simple. It is not. The technical challenges involved in maintaining coherent long-horizon planning while remaining flexible enough to adapt to unexpected results required breakthroughs in memory, context management, and goal representation.
The Planning Stack
Modern agent architectures typically operate across several layers:
Goal Layer: The high-level objective the agent is trying to achieve. This must be represented in a way that allows for verification—how does the agent know when it's done?
Strategy Layer: The general approach to achieving the goal. Multiple strategies may be considered and evaluated before one is selected.
Tactic Layer: The specific sequence of actions to execute the strategy. This is where decomposition happens.
Execution Layer: The actual tool use and API calls that accomplish each tactical step.
Reflection Layer: Continuous monitoring of progress with the ability to backtrack or replan when necessary.
Why Planning Changes Everything
Consider the difference between asking an AI to "build me a web application" with and without planning capabilities.
Without planning: The model attempts to generate an entire application in a single response. It hallucinates file structures, invents APIs, and produces something that looks plausible but doesn't actually work.
With planning: The agent first clarifies requirements. Then it designs an architecture. It creates files one by one, testing each component. When something doesn't work, it debugs. When requirements change, it adapts. The result is something that actually functions.
This is the difference between a party trick and a collaborator.
The Hard Problems
Agent planning isn't solved—it's just beginning. Several fundamental challenges remain:
Credit Assignment: When a multi-step plan fails, which step was responsible? How do you debug a 50-step reasoning chain?
Planning Horizon: How far ahead should an agent plan? Too short and you get myopic behavior. Too long and you get brittle plans that don't survive contact with reality.
Goal Drift: Maintaining focus on the original objective across hundreds of steps without losing the thread or getting distracted by tangential opportunities.
Resource Management: Planning and execution consume compute. How does an agent decide how much to invest in planning versus just trying things?
The Cardinal Approach
At Cardinal, we've been experimenting with what we call "dream heuristics"—a planning methodology that treats the initial plan as a hypothesis to be tested rather than a script to be followed. Combined with our midwifery model for idea development, this allows our agents to balance exploration and exploitation in ways that feel surprisingly human.
More on that in a future post.
What Comes Next
The agents of 2025 are to the chatbots of 2023 what the smartphone was to the feature phone. Same basic form factor, completely different capability profile. We're still early in understanding what these systems can do when properly architected.
The organizations that figure out how to integrate planning-capable agents into their workflows won't just be more efficient. They'll be playing an entirely different game.