Planning Pattern: Working Principle, Workflow and Subtypes
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Before going into the definition and all the technical buzzwords, let’s try to understand from a simple point of view and get the intuition behind the planning pattern.
Think about how you approach a big project—say, organizing a cross-country trip. You wouldn’t just start booking hotels at random. First, you’d break the trip into steps: decide on destinations, book flights, reserve hotels, plan activities, and so on. This ability to decompose a large goal into manageable chunks and to adapt on the fly is what makes you efficient and resilient.
AI agents face similar challenges. Without a plan, they’re like tourists wandering aimlessly. With a planning pattern, they become strategic travelers—goal-oriented, adaptable, and efficient.
The planning pattern is an agentic design pattern where an AI agent autonomously breaks down a complex goal into smaller, actionable subtasks and dynamically sequences them to achieve the desired outcome. This process is called “Task Decomposition”. Think of it as the digital equivalent of making a to-do list but with the added power of real-time reasoning, adaptation, and self-correction.
At its core, the planning pattern gives an agent the ability to think ahead, to map out a path rather than react impulsively. Just as a chess player considers several moves in advance, an agent using the planning pattern anticipates challenges, weighs options, and pivots strategies as circumstances change.
Complexity Handling: Perfect for tasks where the solution isn’t obvious or requires multiple steps.
Adaptability: Enables agents to respond to unexpected changes or failures without getting stuck.
Efficiency: By prioritizing and sequencing tasks intelligently, agents avoid wasted effort.
Scalability: Supports collaboration between multiple agents, each handling specialized subtasks.
Goal Analysis and Context Building
The agent begins by analyzing the overall objective and any constraints or requirements.
It gathers relevant information and builds context, which may involve querying databases, reviewing documents, or interacting with users.
Strategic Task Decomposition
Dependency Mapping and Sequencing
The agent identifies which subtasks depend on others and determines the most logical order for execution.
This step prevents wasted effort and ensures prerequisites are satisfied before moving forward.
Single Agent Task Allocation
The Single Task Agent is responsible for completing each task generated in the previous step.
This agent executes each task using predefined methods like ReAct (Reason + Act) or ReWOo (Reasoning WithOut Observation).
Once a task is completed, the agent returns a Task Result, which is sent back to the planning loop.
Resource Allocation and Tool Integration
The agent selects appropriate tools or external resources for each subtask (e.g., APIs, databases, code interpreters).
It orchestrates tool usage dynamically, matching each task to the best available capability.
Execution and Monitoring
The agent carries out each subtask, monitoring progress and checking for errors or unexpected outcomes.
If a subtask fails or new information arises, the agent can replan, adjust its sequence, or try alternative strategies.
Feedback Loop and Learning
After each action, the agent evaluates the result against the goal.
It collects performance data, learns from mistakes, and updates its plan to improve future outcomes.
Completion and Output Delivery
Once all subtasks are completed and the overall objective is met, the agent compiles and delivers the final output.
The agent may also format results or trigger subsequent workflows as needed.
Enhanced Flexibility: Planning patterns allow agents to dynamically adapt their actions based on changing goals, inputs, or unexpected obstacles, rather than following rigid, pre-set workflows.
Improved Problem-Solving: By decomposing complex tasks into manageable subtasks, agents can systematically tackle multifaceted problems that would overwhelm traditional, single-step agents.
Greater Efficiency: Intelligent sequencing and prioritization of subtasks reduce redundant work and optimize resource allocation, leading to faster and more accurate outcomes.
Resilience and Robustness: Agents can recover from failures or adapt to new information mid-execution, ensuring progress even when initial plans encounter issues.
Scalability: Planning patterns support modular workflows, making it easier to scale up to more complex tasks or coordinate multiple specialized agents.
Planning patterns have several subtypes or categories that can be either implemented remotely or together for a seamless execution. Each category is used for a specific purpose and goal, with the state management and end goal state. Let’s deep dive into various subtypes of the planning pattern
Classical planning is a foundational approach in planning design patterns where the objective is to find a sequence of actions (a plan) that transitions an agent from a specific initial state to a goal state, under the assumptions that the world is static, deterministic, and fully observable
Core Assumptions:
Known initial state.
Deterministic actions without uncertainty.
Full observability.
No concurrency in actions – one at a time
State Representation and State Diagram:
States are typically represented as sets of logical propositions (predicates), and actions/operators have defined preconditions and effects that modify the state
State: A conjunction of predicates or propositions describing the world at a given time
(e.g., At(Truck1, Melbourne) ∧ At(Truck2, Sydney)).
Actions/Operators: Defined by preconditions (what must be true to execute) and effects (how the state changes after execution).
Goal: A set of predicates that must be satisfied in the final state.
Nodes: Represent states (sets of predicates).
Edges: Represent actions that transition from one state to another by applying their effects
Forward State Space Planning (also known as progression planning):
Starts at the initial state and applies applicable actions to generate successor states.
Continues expanding nodes (states) by applying actions until a state satisfying the goal is reached.
Common search algorithms: Breadth-First Search, Depth-First Search, A*, etc.
Backward State Space Planning (also known as regression planning):
Starts at the goal state and works backward, identifying which actions could have produced the current (goal) state.
For each action, it regresses the goal through the action to determine the necessary conditions in the previous state.
Continues until a state is found that matches the initial state.
At each step, the planner determines which actions could achieve the current subgoal and what preconditions must be true before those actions.
Parallel Planning is an approach where multiple actions are executed simultaneously, rather than sequentially, to reach a goal state more efficiently. This paradigm is especially valuable in environments where actions do not interfere with each other and can be performed concurrently, reducing the overall number of time steps required to achieve the objective.
Flow of Parallel Planning
Initial State: Start with a representation of the world.
Action Selection: At each time step, identify all possible actions whose preconditions are satisfied and whose effects do not interfere with each other.
Parallel Execution: Apply the selected set of actions simultaneously, updating the state.
State Transition: Move to the new state resulting from the combined effects of the parallel actions.
Repeat: Continue selecting and executing parallel action sets until the goal state is reached.
Plan Output: The result is a plan where each step may contain multiple actions, reducing the total number of steps compared to sequential planning.
Multi-goal pursuit refers to scenarios where an agent or a group of agents simultaneously works toward achieving multiple goals, which may be independent, overlapping, or even conflicting. In real-world settings, users or agents often pursue several goals concurrently and interleave actions for different goals within the same activity sequence.
Key Features:
Concurrent and Interleaving: Actions for different goals may be mixed within a plan, not strictly separated.
Plan Recognition: Recognizing and managing multiple goals is a challenge, often requiring advanced planning or probabilistic reasoning.
Resource Management: Agents must allocate resources and prioritize among competing or parallel goals.
Synchronous parallel planning is a planning approach where multiple actions are executed at the same time step, but only if they are non-interfering (i.e., their preconditions and effects do not conflict). All agents or sub-plans synchronize at each planning step, and the system waits until all parallel actions are ready to execute before moving to the next step.
Key Features:
Simultaneous Execution: Multiple actions occur together, maximizing efficiency when possible.
Synchronization Point: All actions in a parallel step start and finish together.
Strict Non-Interference: Only actions that do not conflict can be grouped.
Example Flow:
Time Step 1: → {Action A, Action B} (executed in parallel)
Time Step 2: → {Action C, Action D} (executed in parallel)
Use Cases: Robotics (multiple arms working in unison), manufacturing lines, or any system where coordination and timing are critical.
Asynchronous parallel planning allows actions to be executed in parallel, but without requiring synchronization points. Each action or agent can proceed independently as soon as its preconditions are met, regardless of the state of other actions. This approach is more flexible and can lead to faster completion, especially in distributed or loosely coupled systems.
Key Features:
Independent Execution: Actions start as soon as possible, not waiting for others.
No Global Synchronization: Agents or sub-plans do not need to align their steps.
Higher Throughput: Can exploit opportunities for concurrency more aggressively.
Example Flow:
Action A → starts at t=0, completes at t=2
Action B → starts at t=1 (as soon as its preconditions are met), completes at t = 3
Action C → starts at t=2, completes at t=4
Use Cases: Distributed computing, cloud orchestration, and multi-agent systems with independent tasks.
Hierarchical planning is a structured approach to solving complex planning problems by organizing tasks and actions into multiple levels of abstraction or hierarchy. This method allows a system to break down a high-level goal into smaller, more manageable subgoals and tasks, which can then be further refined until primitive, executable actions are reached.
Core Concepts:
Top-Down Decomposition: Breaking high-level goals into progressively smaller subgoals and tasks.
Bottom-Up Composition: Synthesizing lower-level solutions or actions to form higher-level plans.
Multilevel Abstraction: Planning and reasoning occur at various levels of detail, from abstract strategies to concrete actions.
Definition:
This is the primary process in hierarchical planning, where a complex, abstract goal is recursively broken down into subgoals and then into primitive actions.
Flow:
Start with the main (high-level) goal.
Decompose it into a set of subgoals or tasks.
Further decompose each subgoal until reaching actions that the system can directly execute.
At each level, only relevant details are considered, reducing complexity.
Example:
Goal: "Plan a wedding" :
→ Subgoals: Book venue, arrange catering, send invitations
→ Further subgoals: For "Book venue": shortlist venues, visit venues, finalize booking
→ Primitive actions: Call venue, sign contract, make payment
Definition:
This approach works in the reverse direction, where solutions to lower-level tasks are composed to achieve higher-level goals.
Flow:
Solve or plan for the most detailed, concrete tasks first.
Aggregate these solutions to form the solution for their parent subgoals.
Continue aggregating upward until the top-level goal is achieved.
Example:
Primitive actions (e.g., call venue, sign contract)
→ Compose into "Book venue" subgoal
→ Compose all subgoals to complete the "Plan a wedding" goal
Hierarchical planning operates across multiple levels of abstraction, allowing the planner to focus on different granularities of the problem as needed.
Architecture:
High-Level Layer: Abstract goals and strategies (e.g., "organize event")
Mid-Level Layer: Intermediate subgoals (e.g., "arrange logistics")
Low-Level Layer: Concrete, executable actions (e.g., "book taxi").
Benefits:
Reduces computational complexity by narrowing focus at each level.
Supports efficient plan generation, monitoring, and adaptation in dynamic environments.
Probabilistic planning is an approach where an agent must make decisions under uncertainty, specifically when actions can have multiple possible outcomes, each with a certain probability. Unlike classical (deterministic) planning, where the effects of actions are known and predictable, probabilistic planning explicitly models the likelihood of different outcomes, allowing for more robust and realistic decision-making in dynamic environments
Key Features:
Uncertainty Modeling: Actions may lead to different results, each with an associated probability.
Goal: Maximize the expected reward or minimize the expected cost, rather than guaranteeing a specific outcome.
Continuous Belief Space: Probabilities make the state space continuous and potentially infinite, increasing complexity
A Markov Decision Process (MDP) is a mathematical framework for modeling decision-making in environments where outcomes are partly random and partly under the control of a decision-maker.
MDPs provide the formal foundation for probabilistic planning with full observability, modeling the environment’s uncertainty, and guiding the agent to maximize the expected reward.
State Flow in MDP:
Start at the Initial State
Select Action (based on policy)
Transition to Next State (according to transition probabilities)
Receive Reward
Repeat until the goal or terminal state is reached
A Partially Observable Markov Decision Process (POMDP) extends the MDP framework to situations where the agent cannot directly observe the true state of the environment.
POMDPs model planning under both action uncertainty and partial observability, making them essential for real-world problems where the agent does not have perfect information.
State Flow in POMDP:
Agent Maintains Belief State: A probability distribution over possible true states.
Select Action: Based on current belief.
Environment Transitions: To a new (unknown) state, emits an observation.
Agent Updates Belief: Using the observation and transition/observation models.
Repeat: Until the goal or terminal belief is reached.
Temporal planning is an advanced AI planning paradigm where actions are not just sequenced, but also scheduled over time, taking into account their durations, possible overlaps (concurrency), and complex temporal constraints. Unlike classical planning—where actions are considered instantaneous and strictly sequential—temporal planning models the real-world scenario where multiple actions may occur simultaneously, each with its own start and end times, and where timing relationships and deadlines matter
State Representation and flow
Timed State: A state includes not only the current facts about the world but also the current time and the status (active, pending, completed) of all ongoing actions.
Temporal Constraints: Each action or event may have constraints such as earliest start time, latest finish time, or required intervals between actions.
Initial State: Define the starting conditions and time.
Action Selection: Identify which actions can start, considering both logical and temporal preconditions.
Scheduling: Assign start and end times to actions, checking for overlaps and constraint satisfaction.
State Transition: Move to the next state, updating time and the status of all actions.
Goal Check: Repeat until the goal state is achieved within all temporal and resource constraints.
Planning where actions or tasks must be performed within specific time intervals (time windows).
Example: Delivering a package between 10:00 AM and 12:00 PM
Challenges: Coordinating multiple actions to fit within overlapping or tight time windows, especially when resources are shared.
Applications: Logistics, delivery routing, healthcare appointment scheduling.
Scheduling tasks so that each is completed before a specified deadline.
Deadline-Driven Prioritization: Tasks with earlier deadlines are prioritized.
Preemptive Scheduling: Ongoing tasks may be interrupted to ensure critical deadlines are met.
Applications: Real-time systems, multimedia streaming, operating system process scheduling, safety-critical automation.
Temporal planning where actions require limited resources, and plans must ensure no resource is over-allocated at any time.
Resource Allocation: Assigns resources to tasks while considering their availability over time.
Conflict Resolution: Prevents resource contention and ensures all temporal/resource constraints are met.
Applications: Manufacturing, project management, multi-robot coordination, cloud computing
Reactive planning is a type of planning pattern where agents select and execute actions in real-time, responding instantly to changes in their environment rather than following a pre-computed, long-term plan. This approach is ideal for highly dynamic or unpredictable settings, as the agent continuously senses its surroundings and decides the next best action based solely on the current context, often using predefined stimulus-response rules or behavior tables. Unlike classical planning, which generates a full sequence of actions in advance, reactive planning computes just the immediate next action, enabling rapid adaptation but often lacking long-term foresight.
State Representation and flow
Perception: The agent analyzes the environment and gathers current state data.
Action Selection: Based on the current perception, the agent uses rules or behavior tables to choose the next action.
Execution: The chosen action is immediately executed, affecting the environment.
Repeat: The agent loops back to perception, continuously reacting to new stimuli or changes
The agent’s behavior is triggered by specific external or internal events (e.g., obstacle detected, temperature threshold crossed).
Role: Enables the agent to prioritize and respond to critical events as they occur, rather than following a fixed schedule or sequence.
The agent follows a set of policies (mapping from situations to actions) that guide its behavior in different contexts.
Role: Supports flexible and adaptive responses, as the agent can switch policies based on the current state or environment, allowing for more sophisticated and context-aware reactivity.
A layered control system where higher-level behaviors can override or “subsume” lower-level ones.
Role: Each layer handles a different level of behavior (e.g., obstacle avoidance at the lowest, goal-seeking at a higher level), and the most relevant behavior at any moment takes control. This enables robust, emergent behavior from simple, modular rules.
It is a planning approach where agents select, pursue, and adapt their actions to achieve specific objectives, dynamically generating and updating plans based on the current state of the environment and available resources.
This paradigm is exemplified by frameworks like Goal-Oriented Action Planning (GOAP), which models the world as a set of states, defines goals as desired outcomes, and uses planning algorithms (such as A*) to find optimal action sequences that transition the agent from its current state to the goal state. Each action is associated with preconditions (what must be true to execute it) and effects (how it changes the world), and the planner continuously monitors and adapts to changes, replanning if goals or world states shift.
The agent focuses on achieving one specific objective at a time.
The planning algorithm generates the best sequence of actions to reach that goal, updating or replanning if the environment changes or the goal is achieved/interrupted.
Example: In a game, an NPC may have the single goal “find health pack” and will plan all actions around that objective until it is met.
The agent manages and prioritizes multiple goals, which may be independent, overlapping, or even conflicting.
The planner must decompose, sequence, and sometimes interleave actions to pursue several objectives, often optimizing for utility or resource constraints.
Example: A robot in a warehouse may simultaneously pursue “deliver package,” “recharge battery,” and “avoid obstacles,” dynamically adjusting priorities as conditions change.
The agent’s goals or the path to those goals change based on conditions in the environment or the outcomes of previous actions.
The planner adapts in real time, abandoning, switching, or reprioritizing goals as new information emerges or as utility values change.
Example: In the GOAP framework, if an agent’s goal becomes impossible or less valuable due to a new situation (e.g., an enemy appears), it will select a new goal and generate a new plan accordingly.
Prompt Chaining (Sequential) Planning is an AI technique where complex tasks are decomposed into a sequence of simpler, manageable subtasks, each handled by a dedicated prompt. The output of one prompt becomes the input for the next, guiding the AI through a structured, step-by-step reasoning process to achieve a coherent and accurate final result. This approach is especially effective for large language models (LLMs), allowing them to tackle intricate problems in a controlled, transparent, and modular fashion.
Each prompt follows directly from the previous one in a strict, unbranched sequence. The output of step n is always used as the input for step n+1
The next prompt in the sequence is chosen based on the content or evaluation of the previous output, allowing for branching logic or dynamic adaptation within the chain.
Use Case: Useful for tasks that require decision points, error handling, or adaptive reasoning, such as customer support flows (if answer is unclear, ask for clarification; if clear, proceed to next step) or diagnostic processes.
Complexity in Design and Implementation:
Developing adaptive, planning-based AI systems requires significant expertise and effort, especially for large-scale or highly dynamic environments.
Resource Intensive:
These systems often demand substantial computational power, especially for real-time or large-scale applications.
Transparency and Trust:
The decision-making process can become opaque, raising concerns about explainability and trust in automated outcomes.
Ethical and Bias Issues:
Ensuring that planning algorithms are unbiased and ethically sound is a significant challenge, particularly as they are given more autonomy.
Data Dependency:
The effectiveness of planning patterns relies heavily on the quality and completeness of input data.
Greater Autonomy:
As planning patterns mature, AI agents will become increasingly autonomous, capable of making complex decisions with minimal human oversight.
Integration with Multi-Agent Systems:
Future developments will see more collaborative planning among multiple agents, enabling sophisticated teamwork and distributed problem-solving.
Explainable and Trustworthy AI:
Research will focus on making planning decisions more transparent and understandable to users, addressing trust and accountability concerns.
To conclude, planning is the most important step in AI design patterns and workflows. This step enables AI agents to outline the overall plan and act on each result to deliver better output, but it is mainly used with other design patterns like Tool use and Reflection patterns to create end-to-end robust, scalable applications.
