The Planning Problem in Agentic AI: Goals, Tasks, and Constraints in Agentic AI
The Planning Problem in Agentic AI: Goals, Tasks, and Constraints
What “planning” means in agentic systems
When people say an AI agent can “plan”, they usually mean it can take a messy objective ("book my travel", "fix this bug", "prepare a report") and convert it into an ordered set of actions that can actually be executed. In practice, planning is not a single step. It’s a negotiation between goals, constraints, available tools, and the current state of the world.
A strong plan is one that is:
- Actionable (each step is executable)
- Grounded (depends on real observations, not guesses)
- Interruptible (can stop/replan when reality changes)
- Auditable (you can explain why each step exists)
Goals vs tasks vs actions (don’t mix them)
This is the most common mistake in beginner agent designs: they treat goals, tasks, and actions as the same thing.
- Goal: the outcome you want ("Publish a blog post")
- Task: a chunk of work that moves toward the goal ("Draft outline")
- Action: something you can execute with a tool or a human step ("Call /create-doc API")
If your agent writes a plan that contains goals masquerading as actions ("Be creative", "Make it perfect"), it will get stuck.
Constraints: where most plans die
Constraints are the real-world rules: budgets, time, policies, rate limits, data privacy, tool availability, and “do not do” lists. Good agents treat constraints as first-class inputs, not an afterthought.
In production, capture constraints explicitly:
- Allowed tools and scopes
- Max budget (tokens, API calls, money)
- Safety boundaries (no PII leakage, no destructive actions)
- Time windows (deadlines, business hours)
A simple planning template you can reuse
Here’s a practical structure many teams use:
- Clarify goal (write it as a measurable outcome)
- List constraints (hard rules)
- Gather state (what do we know, what must we check?)
- Decompose tasks (subgoals with definitions of done)
- Map actions to tools (each step should be executable)
- Define stop conditions (when do we stop or replan?)
Engineering checklist
- Does every step have an observable output?
- Can the agent recover if a step fails?
- Are we accidentally asking the LLM to “assume” data?
- Do we have time/budget limits and a graceful stop?
Planning is less about writing a fancy list and more about building a system that keeps plans honest.
