Planning

TLDR

Planning is the structured process by which an AI agent bridges the gap between a high-level objective and low-level execution. It serves as a roadmap before execution [src:005], preventing the agent from wandering into "hallucination loops" or inefficient resource usage. Modern agentic planning is categorized into explicit planning (symbolic, textual, and verifiable) and implicit planning (learned behavioral patterns) [src:005]. While classical systems relied on formal languages like PDDL, contemporary agents leverage Large Language Models (LLMs) through techniques like Chain of Thought (CoT) and Tree of Thoughts (ToT) to decompose complex tasks into manageable sub-goals.

Conceptual Overview

At its core, planning is a systematic mechanism for defining objectives, mapping paths, and establishing tracking mechanisms. In the context of AI agents, it is the "pre-computation" of a sequence of actions intended to reach a specific state. Without a planning layer, an agent is merely reactive—responding to immediate stimuli without a sense of long-term trajectory.

The Roadmap Metaphor

Planning is fundamentally about creating a roadmap before execution [src:005]. Rather than beginning work without direction, agents develop comprehensive plans that guide decision-making, resource allocation, and team coordination. This process involves:

1. Goal Definition: Identifying the desired end-state.
2. Scope Establishment: Determining the boundaries of the task.
3. Resource Identification: Assessing available tools, APIs, or data.
4. Task Breakdown: Decomposing a "Strategic" goal into "Tactical" operations.

Explicit vs. Implicit Planning

The distinction between explicit and implicit planning is critical for agent architecture:

• Explicit Planning: The agent generates a readable, symbolic, or textual representation of its intended steps. This is verifiable and allows for human-in-the-loop intervention. For example, an agent might output a JSON list of five steps before calling any tools.
• Implicit Planning: The agent’s "plan" is embedded within its weights or learned associations. It "knows" what to do next based on the current context without necessarily articulating a multi-step roadmap. This is often more efficient but lacks transparency [src:005].

Observer-Aware Planning

In multi-agent or human-agent environments, planning must account for how actions are perceived. The Observer-Aware Markov Decision Process (OAMDP) framework suggests that an agent's plan is only successful if it achieves the goal and allows observers to correctly infer the agent's intent [src:001]. This prevents "surprising" behavior that could lead to collisions or coordination failures in shared environments.

Infographic Description: A flowchart titled "The Agentic Planning Cycle." It starts with a central "Goal" node.

1. Decomposition: The goal splits into three sub-tasks (Sub-task A, B, C).
2. Selection: A "Reasoning Engine" evaluates tools for each sub-task.
3. Execution: The agent performs Sub-task A.
4. Observation/Feedback: The result of A is fed back into the "Reasoning Engine."
5. Re-planning: If the result of A changes the environment unexpectedly, the agent modifies Sub-tasks B and C. Sidebars contrast "Explicit" (textual logs) vs "Implicit" (neural activations) pathways.

Practical Implementations

Implementing planning in modern AI agents typically involves leveraging the reasoning capabilities of LLMs.

LLM Reasoning Paradigms

Several prompting strategies have emerged to facilitate planning:

• Chain of Thought (CoT): Encourages the agent to "think step-by-step." This is the simplest form of explicit planning, where the agent writes out its reasoning before providing an answer.
• Tree of Thoughts (ToT): A more advanced version where the agent explores multiple reasoning branches simultaneously. If one branch leads to a dead end, the agent backtracks—a classic planning behavior.
• ReAct (Reason + Act): Combines reasoning traces with action-specific steps. The agent writes a "Thought," performs an "Action," receives an "Observation," and then updates its "Plan."

Task Decomposition and ANN

For complex tasks, agents use Task Decomposition. A "Manager" agent might break a request like "Research and write a 2000-word report on fusion energy" into sub-tasks: search, outline, draft, and cite. To optimize this, agents often use ANN (Approximate Nearest Neighbor) search. When faced with a new task, the agent can query a vector database of "past successful plans" using ANN to find a similar roadmap that worked previously. This allows the agent to "remember" how to plan effectively without starting from scratch every time.

Comparing Prompt Variants (A)

In production environments, developers use A (Comparing prompt variants) to determine which planning structure works best. For instance, one might compare a "Zero-shot Planner" against a "Few-shot Planner" that includes examples of successful task breakdowns. Systematic testing (A/B testing for prompts) is essential because small changes in the planning prompt can lead to significant differences in execution success.

Advanced Techniques

Hierarchical Task Networks (HTN)

HTN is a classical planning methodology that is seeing a resurgence in neuro-symbolic agents. It involves a hierarchy of tasks, where "Complex Tasks" are decomposed into "Primitive Tasks" that the agent can execute directly. This provides a rigid but highly reliable structure for domains where safety and predictability are paramount.

PDDL (Planning Domain Definition Language)

PDDL is the "gold standard" for formal planning. It defines:

• Objects: The things in the world.
• Predicates: Properties of objects (e.g., is-at robot location-A).
• Actions: What the agent can do, defined by preconditions and effects. While LLMs are flexible, they often struggle with the strict logic required for PDDL. Advanced agents use LLMs to generate PDDL code, which is then solved by a deterministic "Classical Planner" to ensure the resulting roadmap is mathematically sound.

Observer-Aware Decision Processes (OAMDP)

As mentioned in the research context, OAMDPs extend standard decision models by incorporating the observer's beliefs into the reward function [src:001]. Mathematically, the agent maximizes: $$R_{total} = R_{task}(s, a) + \alpha \cdot P(Intent | Actions)$$ Where $\alpha$ is a weight representing how much the agent cares about being "understandable." This is vital for autonomous vehicles or collaborative robots that need to signal their intent through their movement patterns [src:008].

Contextual Assumption Management

Planning models operate under assumptions about the environment (e.g., "the API will always return a 200 status"). Contextual Assumption Management involves surfacing these assumptions explicitly [src:004]. If an assumption is violated (e.g., the API is down), the agent triggers a "Contingency Plan" rather than failing blindly.

Research and Future Directions

World Models and Simulation

The future of planning lies in World Models. Instead of just predicting the next word, agents will maintain a latent simulation of the world. They can "run" a plan in their internal simulation to see if it fails before ever taking an action in the real world. This "look-ahead" capability is what separates human-level planning from current autoregressive models.

Neuro-Symbolic Integration

There is a growing trend toward combining the "intuition" of neural networks (LLMs) with the "rigor" of symbolic logic (PDDL). In this hybrid model, the LLM acts as the "Intuitive Planner" (System 1), while a symbolic engine acts as the "Logical Verifier" (System 2). This addresses the issue of LLMs creating plans that look plausible but are physically or logically impossible.

Long-Horizon Reasoning

Current agents struggle with tasks that require hundreds of steps. Research into Memory-Augmented Planning aims to solve this by allowing agents to store intermediate states in long-term memory, using ANN to retrieve relevant context when the plan spans days or weeks.

Implicit Intelligence and Intent Recognition

As agents become more sophisticated, they will need to master Implicit Intelligence—the ability to infer unstated requirements [src:006]. If a user says "Plan a trip to London," the agent should implicitly plan for travel insurance, currency exchange, and weather-appropriate clothing without being explicitly told to do so.

Frequently Asked Questions

Q: What is the difference between a "Plan" and a "Policy"?

A Plan is a specific sequence of actions intended for a specific goal (e.g., "Go to the kitchen, grab a cup, pour coffee"). A Policy is a general mapping from any state to an action (e.g., "If you are thirsty, find liquid"). Planning is often used to generate a policy or to find a path when a pre-existing policy doesn't cover the current situation.

Q: Why do LLM agents sometimes fail at simple planning tasks?

LLMs are probabilistic, not deterministic. They may suffer from "look-ahead" issues where they choose a step that seems correct now but makes the final goal impossible. This is why techniques like Tree of Thoughts and Backtracking are necessary to correct the agent's trajectory.

Q: How does "A" (Comparing prompt variants) help in planning?

By systematically comparing prompt variants (A), developers can identify which linguistic structures (e.g., "Think like a project manager" vs. "List all constraints first") minimize logical errors in the agent's roadmap. It turns prompt engineering into an empirical science.

Q: Can an agent plan in a partially observable environment?

Yes, this is typically handled via POMDPs (Partially Observable Markov Decision Processes). The agent's plan must include "Information Gathering" actions (e.g., "Look inside the box") to reduce uncertainty before committing to a final sequence of goal-oriented actions.

Q: What is the role of ANN in agentic planning?

ANN (Approximate Nearest Neighbor) allows an agent to quickly search through millions of previous experiences or "plan templates" to find one that matches the current context. This "Case-Based Reasoning" makes planning much faster and more reliable than generating a new plan from scratch every time.