🦸🏻#11: How Do Agents Plan and Reason?

Last week, we explored whether GenAI can truly reason, categorizing human thinking modes to assess AI's reasoning abilities. Today, we discuss reasoning and planning. Reasoning in isolation is often not enough – the AI also needs a plan for how to apply that reasoning to achieve a goal. Planning provides structure, order, and goal-direction to the reasoning process. Without it, even a very intelligent model might flail on complex tasks, producing partial or disorganized answers. Large language models (LLMs) have begun to interface with planning mechanisms, either internally (through prompt techniques that simulate planning) or externally (by working with dedicated planning modules or tool APIs). The result is AI agents that can reason through problems and then act on those reasoning steps in an organized way. This combination is opening up real-world applications from personal assistants to autonomous robots, where reasoning guides actions in a plan — very much how human intelligence operates with both thought and action hand in hand.

As an example, we examine DeepSeek’s efforts to enhance its models' reasoning capabilities. This article is a long read, and at the end, you'll find an extensive list for further exploration into Reasoning and Planning. As this field rapidly evolves, we anticipate breakthroughs that will enable AI agents and systems to reason more effectively and plan with greater autonomy and precision. These advancements could lead to AI that not only understands complex scenarios but also executes multi-step tasks seamlessly, dynamically adapting as new information emerges. The potential applications? Endless.

What’s in today’s episode?

• Brief history overview

• Understanding AI reasoning

• Recent breakthroughs in reasoning

  • Chain-of-Thought prompting

  • Self-Reflection and Self-Consistency

  • Few-Shot and In-Context Learning

  • Neuro-Symbolic approaches

• Reasoning is impossible without planning

• Main planning techniques for precision and adaptability

  • Classical AI planning (Deliberative planning)

  • Reinforcement learning (with DeepSeek R1 as an example)

  • Hierarchical planning (Tiered strategies)

• Concluding Thoughts

• Resources that were used to write this article (we put all the links in that section)

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Brief history overview

Early AI research saw reasoning as the key to machine intelligence, but scaling general reasoning proved an unsolvable challenge for decades. From the 1950s to the late 1980s, symbolic AI sought to encode logic and rules explicitly, producing systems capable of theorem proving and medical diagnosis. However, these systems struggled with real-world ambiguity and lacked adaptability.

Then came expert systems. While they excelled in narrow tasks – like medical diagnosis (MYCIN) and computer configuration (XCON) – they relied on handcrafted rules and couldn’t generalize or adapt to new situations.

By the 1990s, many AI researchers turned to machine learning and statistical methods, which excelled at pattern recognition but largely sidestepped explicit reasoning. Problems like vision and speech, once considered harder, saw progress with neural networks, while abstract reasoning and common sense remained unsolved. This era highlighted a paradox (known as “Moravec’s Paradox”): tasks requiring formal reasoning (like playing chess or solving equations) were easier for computers than everyday reasoning. Classic high-level reasoning could sometimes be brute-forced (Deep Blue beat humans at chess by exploring millions of moves), but replicating the flexible, knowledge-driven reasoning of a human child was far out of reach.

Throughout these years, AI has gone through multiple winters (this is our favorite article about all four AI winters), with symbolic AI taking particularly hard hits. Yet early symbolic reasoning efforts laid important foundations and are now resurfacing in hybrid approaches, such as neurosymbolic AI and retrieval-augmented generation (RAG). These methods combine rule-based reasoning with modern data-driven techniques, underscoring how difficult general reasoning remains in an open-ended world (a chapter about open-endedness).

Understanding AI Reasoning

AI reasoning (for more detailed definition of Reasoning and Modes of thinking please refer to our previous article) involves drawing conclusions based on facts, rules, or evidence. Traditional key types include:

• Deductive: Applying general rules to specific cases (e.g., “All birds have wings; a sparrow is a bird, so it has wings”).

• Inductive: Inferring general patterns from examples.

• Abductive: Making educated guesses from incomplete data, like diagnosing symptoms.

• Probabilistic: Managing uncertainty with probabilities, as in Bayesian inference.

AI spans strict logic to flexible pattern recognition. While LLMs don’t truly “reason” like humans, they can perform well with the right prompts. For years, pure neural networks were thought to lack advanced reasoning, but recent breakthroughs have changed that. Models like OpenAI’s o1, o3, and DeepSeek R1 demonstrate impressive reasoning capabilities, making it a hot topic. What innovations and research have driven this progress? Let’s explore →

Recent Breakthroughs in Reasoning