Planning for AI Agents: Overcoming the Limitations of Planning in LLM-Powered AI-Agents

Welcome to the latest edition of #AllThingsAI newsletter and to the 3rd part of our comprehensive series on #AIAgents, where we are discussing the limitations of planning and reasoning among AI-Agents and how to overcome these limitations.

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When it comes to AI-powered agents, especially those built using Large Language Models (LLMs), the buzzword "planning" often emerges as a crucial element in their performance and reliability. But what does "planning" mean for an AI agent, and why is it so hard to get right? Developers frequently cite three critical limitations when it comes to building effective agents: planning, user experience (UX), and memory. Among these, the ability to plan and reason—particularly for more complex tasks—remains one of the most significant hurdles.

In this article, we’ll break down what planning and reasoning actually mean for an agent, why it remains such a big challenge, and how developers are currently tackling this problem. We’ll also explore what the future may hold for planning and reasoning in AI, touching on advancements in general and domain-specific cognitive architectures that promise to shape the next wave of AI agents.

What Is Planning and Reasoning for AI Agents?

At its core, planning for an AI agent refers to its ability to decide what actions to take, both in the short term and the long term. It involves evaluating available information, determining a series of steps required to achieve a goal, and then choosing the first action to execute. For humans, this might feel intuitive, but for LLMs, it's a complex challenge.

LLMs often rely on a technique called function calling (or tool calling) to choose immediate actions. Introduced by OpenAI in mid-2023 and adopted by other platforms soon after, function calling enables developers to pass JSON schemas to the LLM, letting it match outputs to these schemas. While this helps with short-term decisions, it becomes significantly harder to accomplish long-term planning. Why? The model must switch between thinking about a big-picture goal and focusing on immediate actions—a balancing act that many LLMs struggle with.

Additionally, the more actions an agent takes, the more information it has to process, often leading to issues with context window size. The agent can get “distracted,” as larger context windows feed too much information back into the model, negatively affecting performance. This results in a well-documented problem: LLMs don’t reason and plan as well as they need to for real-world tasks, particularly complex ones.

Current Fixes to Improve Agent Planning

So, how are developers addressing this? The first, and often simplest, solution is to make sure that the LLM has all the information it needs to plan effectively. While this sounds obvious, many times, the prompt passed into the LLM lacks the necessary details for it to make reasonable decisions. By adding a retrieval step or refining prompt instructions, developers can provide more accurate data and context.

Beyond prompt adjustments, developers are also exploring changes to the cognitive architecture of their agents. Cognitive architectures refer to the underlying logic that an application uses to reason, and there are two main types:

• General-purpose cognitive architectures: These are designed to improve reasoning across a wide range of tasks. A common example is the "plan and solve" architecture, which splits tasks into a planning phase and an execution phase. Another example is the Reflexion architecture, where agents reflect on the correctness of their previous actions before deciding the next step.
• Domain-specific cognitive architectures: These are tailored to specific types of problems or domains. Unlike general-purpose architectures, these frameworks provide custom logic and workflows for narrowly defined tasks.

For example, the AlphaCodium system, which excels in code generation, has a cognitive architecture that includes domain-specific steps such as writing tests, generating code, and iterating based on test results. This type of architecture wouldn’t work for, say, essay writing, but it’s highly effective for coding tasks.

Why Domain-Specific Cognitive Architectures Work So Well

Domain-specific cognitive architectures offer a more tailored approach to planning. Think of it as giving the agent more explicit instructions on how to behave, removing some of the planning burden from the LLM itself. Instead of relying on the model to come up with a plan on its own, developers create a detailed blueprint for the task.

Nearly all the advanced “agents” we see in production actually have a very domain specific and custom cognitive architecture.

There are two ways to think about this approach:

1. Explicit communication: You can view this as another method of instructing the agent. Whether through prompt engineering or coding specific workflows, both methods serve to guide the LLM in executing a particular task.
2. Engineer-led planning: Essentially, developers are saying to the LLM, "Let me handle the planning; you just follow these steps." By doing so, they remove a portion of the planning responsibility from the LLM, increasing the chances that the task will be completed successfully. This can be seen in the AlphaCodium example, where the agent’s steps are predefined in a highly specific sequence designed by engineers.

Nearly all advanced AI agents deployed in production today are built using domain-specific cognitive architectures. These custom designs simplify complex workflows and make agents more reliable, as the LLM doesn't need to independently reason through every step.

The Future of Planning and Reasoning for AI Agents

The landscape of LLMs is evolving rapidly. As models become faster, cheaper, and more intelligent—thanks to improvements in scale and research breakthroughs—planning and reasoning will undoubtedly improve. But will general-purpose LLMs ever fully solve this problem?

Our best guess is that while LLMs will get better at reasoning, custom architectures will continue to play an essential role. Even with more intelligent models, developers will still need to communicate task-specific instructions, either through improved prompts or cognitive architectures coded into the system. For simple tasks, a well-crafted prompt may suffice, but for more complex problems, relying on code-first approaches can offer faster, more reliable, and easily debuggable solutions.

n short, the future of planning and reasoning will likely involve a combination of improved LLM capabilities and custom, domain-specific cognitive architectures. As tools like LangGraph emerge, offering greater control and flexibility, we’ll see even more developers building agents that can handle complex, task-specific planning with precision.

Your Turn: Where Do You See Planning for AI Agents Going?

As LLM technology continues to evolve, how do you envision the future of planning and reasoning for AI agents? Will general-purpose models eventually master complex reasoning, or will custom architectures remain critical to their success?

Share your thoughts in the comments below. What are the biggest pain points you’ve encountered when building agents, and how are you tackling them? Let’s get the conversation started! ??

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