Reasoning Models (o1) and the Future of Agentic Thought

Key Findings

•Internal Monologue: Reasoning models like Gemini's o1 utilize hidden chains of thought to solve complex logic and coding problems.
•Slow Thinking: The shift from instantaneous generation to "thinking time" allows agents to handle significantly more difficult tasks.
•Error Correction: Built-in reasoning loops reduce logical fallacies and hallucinations without requiring external workflow orchestration.
•Computational Cost: The increased accuracy of o1-style models comes with higher inference costs and latency, requiring strategic deployment.

The System 1 vs. System 2 Divide

Standard LLMs operate primarily on "System 1" thinking—fast, intuitive, and pattern-based. While impressive, they often fail at tasks requiring deep logic or multi-step planning (e.g., complex math or debugging).

Reasoning Models (like o1) introduce "System 2" thinking—slow, deliberate, and logical.

How o1 Enhances Agentic Workflows

In a standard agentic workflow, the "Reasoning" part of the loop (ReAct) is performed by a standard LLM. By replacing the base model with a Reasoning Model, we get:

Jan 2026 Refresh: Chain of Thought (CoT) Pruning

A major advancement in January 2026 is Reasoning Trace Pruning. Advanced reasoning models now selectively hide or reveal parts of their internal chain of thought to sub-agents based on the complexity of the delegated task. This prevents "Information Overload" in Worker agents while maintaining the high-level logical integrity of the Manager agent's plan.

•Better Planning: The model identifies edge cases before it starts executing.
•Superior Tool Choice: The model reasons about why it needs a tool, leading to fewer wasted API calls.
•Implicit Self-Reflection: The model critiques its own plan during the "thinking" phase.

Comparison: Standard LLM vs. Reasoning Model

Task	Standard LLM (GPT-4o)	Reasoning Model (o1)
Logic Puzzles	Hits a ceiling quickly	Solves Strategic Intelligence logic
Code Refactoring	Functional but potentially buggy	High-integrity, optimized code
Strategic Planning	Linear and predictable	Multidimensional and robust
Execution Speed	Instantaneous	10-60 seconds (Thinking)

The "Thinking" Phase Visualized

Technical Implementation: Using o1 in Agents (Python)

import openai

# Note the use of reasoning_effort parameter in 2026 models
response = openai.chat.completions.create(
    model="o1-preview",
    messages=[{"role": "user", "content": "Design a secure multi-tenant architecture for a financial agent."}],
    reasoning_effort="high"
)

print(f"Thinking tokens used: {response.usage.completion_tokens_details.reasoning_tokens}")

The Role of Reasoning in Autonomous Discovery

Reasoning models are particularly effective in scientific discovery and advanced engineering. By allowing the agent to "ruminate" on a problem, we enable it to find non-obvious solutions that pattern-matching models would miss.

Multi-Modal Reasoning Loops

Reasoning models have now integrated visual and auditory "thinking." In 2026, an o1-style model can "visualize" a physical repair process or "hear" a complex piece of code by reasoning over audio-spectral embeddings, allowing it to solve problems that require spatial or temporal logic that was previously beyond the reach of text-only models.

Technical Spoke Directory

Conclusion: Quality Over Speed

The rise of reasoning models signals a shift in the AI industry. We are moving away from a race for the fastest response toward a race for the most correct response. For agentic AI, this means more reliable, more capable, and more trustworthy autonomous systems.

Citations: OpenAI (2024) "Learning to Reason with LLMs", Kahneman (2011) "Thinking, Fast and Slow".

See Also: The Referential Graph