What is dynamic planning in AI agents?

Dynamic planning in AI agents means creating or updating the plan during execution as new information, failures, constraints, or opportunities appear.

Instead of treating the original plan as fixed, the agent continuously uses the latest state to decide what should happen next.

A simple example

Suppose an agent plans:

1. search for a flight
2. book the selected flight
3. send confirmation

But the booking tool returns:

“Flight no longer available.”

A dynamically planning agent can change the remaining plan:

1. search for alternative flights
2. compare the new options
3. ask the user to choose if needed
4. continue booking

The goal stays the same, but the route to the goal changes.

Dynamic planning uses new observations

After each important action, the agent may receive new information from:

• tools
• APIs
• databases
• users
• workflow state
• external events

That information can change what the next step should be.

The plan does not need to be fully known upfront

For uncertain tasks, the agent may only plan the next few useful steps.

For example:

1. inspect the problem
2. gather missing information
3. decide the next action based on what was found

This avoids creating detailed steps based on assumptions that may quickly become invalid.

Dynamic planning is useful when conditions can change

It is especially useful when:

• external data changes
• tool calls can fail
• multiple paths are possible
• user input may arrive later
• resource availability is uncertain
• the task is long-running

For example, an agent managing a delivery workflow may need to change its plan if the selected courier becomes unavailable.

Completed work should usually be preserved

Dynamic planning does not mean restarting everything whenever something changes.

Suppose:

• customer data retrieved — completed
• report generated — completed
• email send — failed

The agent should normally revise the remaining steps around the failure rather than repeat successful work.

New information can remove steps

Dynamic planning can make a plan shorter.

For example, the original plan includes:

1. search customer
2. retrieve customer ID
3. retrieve orders

But the user then provides the customer ID directly.

The agent can remove the unnecessary search step.

New information can add steps

It can also expand the plan.

Suppose the agent discovers that an action requires manager approval.

The plan may change from:

validate request → execute action

to:

validate request → request approval → execute action

Branches are often resolved dynamically

A plan may contain a decision such as:

Check inventory

If available:

• create order

If unavailable:

• find alternatives

The actual path becomes clear only after the tool returns its result.

Dynamic planning lets the agent follow the correct branch using current observations.

Failures can trigger replanning

If a step fails, the agent should determine whether to:

• retry
• use a fallback tool
• choose another path
• ask the user
• stop safely

For example, if one data source is unavailable, the plan may switch to an approved backup source.

Constraints can change the plan

The agent may discover new constraints during execution, such as:

• permission denied
• budget exceeded
• deadline changed
• resource unavailable
• approval required

A dynamic planner updates the remaining steps rather than continuing with an invalid plan.

Dynamic planning still needs boundaries

The agent should not change plans without limits.

The system may enforce:

• maximum number of replans
• allowed tools
• approval requirements
• cost limits
• time limits
• stopping conditions

This prevents endless planning loops.

Dynamic planning is different from random improvisation

The agent should not simply choose unrelated actions whenever something changes.

Each plan update should still be connected to:

• the original goal
• the current state
• completed work
• known constraints

The new plan should explain what remains necessary to reach the goal.

A practical dynamic-planning loop

A dynamic agent may work like this:

1. Understand the goal.
2. Create an initial plan.
3. Execute the next valid step.
4. Observe the result.
5. Update the current state.
6. Check whether the remaining plan is still valid.
7. Modify the plan if needed.
8. Continue until the goal is complete or cannot safely proceed.

In short

Dynamic planning is the ability of an AI agent to adapt its plan while the task is running.

The agent uses new observations to add, remove, reorder, or replace future steps while preserving completed work and continuing toward the same goal. It is most useful when tasks involve uncertainty, changing conditions, branching, or failures.