Future AGI

Future AGI: The LLM Observability and Evaluation Platform

What is Future AGI? Future AGI is a comprehensive platform designed to help enterprises achieve high accuracy in their AI applications. It focuses on observability, evaluation, and optimization of large language models (LLMs) and AI agents, ensuring trustworthy, accurate, and responsible AI.

Key Features and Benefits

• AI Evaluation: Assess and measure agent performance with proprietary evaluation metrics to pinpoint root causes and incorporate actionable feedback.
• AI Optimization: Enhance LLM application performance by refining prompts based on feedback from evaluations or custom input. The system automatically adjusts the prompt for optimal results.
• AI Monitoring & Protection: Track applications in production with real-time insights, diagnose issues, and improve robustness. Gain access to Future AGI's safety metrics to block unsafe content with minimal latency.
• Multimodal Evaluation: Evaluate AI across different modalities, including text, image, audio, and video. Identify errors and automatically get feedback to improve performance.
• Integration: Seamlessly integrate Future AGI into existing workflows with industry-standard tools. This developer-first approach ensures minimal disruption to your team's processes.
• Synthetic Datasets: Generate and manage diverse synthetic datasets to effectively train and test AI models, especially for handling edge cases. Datasets can be fully customized.
• Experimentation: Test and compare multiple agentic workflow configurations to identify the 'Winner' based on built-in or custom evaluation metrics – all without writing any code.

How does Future AGI Work?

Future AGI's platform offers a suite of tools that cover the entire AI development lifecycle:

1. Build: Leverage Future AGI to construct AI models, ensuring they are robust and reliable from the outset.
2. Evaluate: Utilize built-in evaluation metrics to rigorously assess the performance of your AI agents, identifying areas for improvement.
3. Experiment: Conduct A/B testing with different configurations to determine the optimal setup for your AI workflows.
4. Optimize: Fine-tune your AI models based on evaluation feedback, allowing the system to automatically refine prompts for enhanced performance.
5. Observe: Monitor your AI applications in real-time, gaining valuable insights into their behavior and identifying potential issues.
6. Protect: Implement safety measures to block unsafe content and ensure responsible AI practices.

Integration Example:

Future AGI integrates easily with existing development workflows. Here’s an example of how to integrate it with OpenAI:

## pip install traceAI-openai
import os

os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
os.environ["FI_API_KEY"] = "your-futureagi-api-key"
os.environ["FI_SECRET_KEY"] = "your-futureagi-secret-key"

from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType

trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="openai_project",
)

from traceai_openai import OpenAIInstrumentor

OpenAIInstrumentor().instrument(tracer_provider=trace_provider)


import base64
import httpx
from openai import OpenAI

client = OpenAI()

image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
image_media_type = "image/jpeg"
image_data = base64.standard_b64encode(httpx.get(image_url).content).decode("utf-8")

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
                    },
                },
            ],
        },
    ],
)

print(response.choices[0].message.content)

Customer Success and Case Studies

Several case studies highlight the effectiveness of Future AGI. For example, one case study demonstrated a 50% increase in summary quality and a 10x faster summary evaluation process.

• Elevating SQL Accuracy: Future AGI streamlined retail analytics, enhancing the accuracy of SQL queries.
• Enhancing Meeting Summarization: Future AGI’s intelligent evaluation framework improved the quality and speed of meeting summarization.

Why is Future AGI Important?

Future AGI addresses the probabilistic nature of LLMs by providing tools to reliably build, evaluate, and improve AI. It enables developers to:

• Achieve higher model accuracy in production.
• Accelerate AI evaluation and agent optimization.
• Ensure responsible AI practices.

Who is Future AGI For?

Future AGI is designed for developers, data scientists, and AI engineers who need to build and deploy accurate and reliable AI applications. It is particularly useful for:

• Enterprises building AI solutions across various modalities (text, image, audio, video).
• Teams looking to integrate AI into existing workflows seamlessly.
• Organizations prioritizing AI safety and responsible AI practices.

Conclusion

Future AGI is a valuable platform for organizations seeking to enhance the accuracy, reliability, and safety of their AI applications. By providing comprehensive tools for evaluation, optimization, and monitoring, Future AGI enables developers to ship AI to production faster and with greater confidence. It supports various modalities and integrates seamlessly with existing workflows, making it a versatile solution for diverse AI needs.