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The Most Accurate LLM for AI Agents.

TheAgenticAI uses a mixture of open-source models, combined with online reinforcement learning to deliver exceptional accuracy for Agentic AI Workflows.

Multi-Step Reasoning

Long-Context

Function-Calling

More Accurate

than Claude3.5-Sonnet or GPT4

Outperforms Claude 3.5 Sonnet/Opus and GPT-4 by a mile.

No API changes required.

On complex agentic reasoning tasks, combining multi-step workflows with function-calling, code generation and structured outputs.

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What Problem are We Solving?

Current SOTA LLMs fall short on accuracy for moderate to complex reasoning tasks, especially if function/tool-calling is involved.


For simple operations, models like GPT4 and Claude3.5-Sonnet can get more than 90% accuracy, but that quickly falls to a mere 60% - 65% range on multi-step reasoning tasks, rendering them highly unreliable.

Let’s be honest, 60% - 65% accuracy is not something your users would want in production.

TheAgentic.AI Approach

TheAgenticAI's MoA approach combined with online reinforcement learning routinely gets accuracy to 77% and more on complex reasoning tasks when GPT4 and Claude3.5 perform in the 60% - 65% range on complex reasoning tasks.

And the best part is–

It involves no modification to your existing pipelines.

Available via OpenAI SDK compatible API

We Have Numbers Backing Our Theory

We’ve put our claims to the test by evaluating GPT-4, Claude 3.5 Sonnet, and TheAgenticAI Ensemble on the T-Eval Test— a comprehensive benchmark designed to assess the reasoning and tool-calling capabilities of LLMs across complex domains such as structuring, planning, and multi-step reasoning.

Source: T-Eval Test Benchmark

Benchmark Results: Reason-Retrieve-Understand Task

Thought (Score for Chain of Thought)

Measures: The quality of reasoning in function-calling tasks.

Interpretation: A higher score indicates more coherent and logical decision-making.

Name (Score for Function Name)

Measures: Accuracy in identifying and selecting the correct function.

Interpretation: A higher score signifies greater precision in choosing the right function.

Arguments (Score for Function Arguments)

Measures: Accuracy in passing correct arguments to the selected functions.

Interpretation: A higher score reflects more accurate and appropriate input handling.

Results Breakdown:

GPT-4

  • Out of 100 input sequences, GPT-4 correctly reasoned the chain of thought in about 65 cases.


  • For tool-calling, it identified the correct function 82% of the time (82 out of 100 cases). It provided the correct arguments to only 75% of the correctly identified functions (61 out of 82 sequences).


  • In total, its tool-calling was successful for about 61 sequences out of 100.

TheAgenticAI Ensemble

  • The model demonstrated superior performance, correctly reasoning for 84 out of 100 cases.


  • It could identify the correct function to call 92% of the time (92 out of 100 cases), and provided the correct arguments for 87% of these 92 cases.


  • Thereby, making accurate function calls in about 80 out of 100 instances.


Its reasoning and tool-calling abilities significantly outperformed the current state-of-the-art (SOTA) models.

Where TheAgenticAI’s Approach Shines?

Single-step Reasoning | complex output

Single-step Reasoning | Simple output

Multi-step Reasoning | simple output

Multi-step Reasoning | complex output