Future AGI: Observability and Evaluation Platform for AI Applications

Future AGI is a comprehensive AI lifecycle management platform designed to help developers and enterprises build, evaluate, monitor, and optimize their AI applications, especially those using Large Language Models (LLMs). The platform provides a full suite of tools from data generation and model experimentation to real-time monitoring in production environments, with the core goal of addressing the challenges of accuracy, reliability, and security for AI applications, especially intelligences. With automated evaluation tools, detailed behavioral tracking (observability), and security guardrails, Future AGI strives to reduce the development iteration time of AI applications from days to minutes, helping teams bring reliable AI capabilities to market faster.

Function List

• Dataset management. Supports uploading existing data (CSV/JSON format) or generating high-quality synthetic data for comprehensive training and testing of AI models, effectively covering a wide range of edge cases.
• Experiment. Provides a coding-free visual interface for testing, comparing and analyzing multiple AI Workflow configurations and identifying the best solution based on built-in or custom evaluation metrics.
• Evaluate. It is able to automatically assess and measure the performance of AI intelligences, pinpoint the root cause of the problem, and continuously improve it through actionable feedback. The platform has a self-developed evaluation model that outperforms the industry's mainstream models in a variety of scenarios.
• Optimize and Improve. Based on the evaluation results or user feedback, the system can automatically optimize and refine the prompt words (Prompt) to improve the overall performance of the AI application.
• Monitor & Protect. Application performance metrics are tracked in real-time in production environments, providing insights to diagnose potential problems. At the same time, the platform provides security metrics that can stop the generation of insecure content with very low latency, protecting systems and users.
• Custom and Multimodal support. Evaluation capabilities cover a wide range of data types such as text, image, audio and video to accurately identify and provide feedback on errors in multimodal applications.
• Integrate. Developer-centric and easily integrated into existing workflows with support for OpenAI, the Anthropic, LangChain, Vertex AI and other industry standard tools and frameworks.

Using Help

The Future AGI platform is designed with a core focus on simplifying the process of developing and maintaining AI applications. Users can do this through the SDK (Software Development Kit) it provides or directly on the visualization interface. The following is the basic process for accessing Future AGI's Observability features based on the Python SDK, which is the recommended starting point for getting started with the platform.

Step 1: Environment Preparation and Installation

First, you need to install the Python library provided by Future AGI. This library is used to track and log all operations related to the Large Language Model (LLM) in your AI application. Open your terminal or command line tool and execute the following pip command:

# 假设库名为 traceai-openai，具体请参照官方文档
pip install traceAI-openai

Step 2: Obtain and Configure API Key

In order for your application to communicate with the Future AGI platform, you need three key credentials:OPENAI_API_KEY(if you are using OpenAI models),FI_API_KEYcap (a poem)FI_SECRET_KEYThe last two keys need to be obtained after logging in to the Future AGI platform. The latter two keys need to be obtained in the project settings after logging into the Future AGI platform.

After obtaining the keys, the best practice is to set them as environment variables to avoid hard-coding sensitive information in your code.

export OPENAI_API_KEY="your-openai-api-key"
export FI_API_KEY="your-futureagi-api-key"
export FI_SECRET_KEY="your-futureagi-secret-key"

Step 3: Integrate tracking in your code

After configuring the key, you need to initialize Future AGI's tracking service in your Python code. This usually takes only a few lines of code.

1. Importing the necessary modules:
   You need to start withfi_instrumentationImporting from librariesregistercap (a poem)ProjectTypeand importoslibrary to read environment variables.

   import os
   from fi_instrumentation import register, ProjectType
   from traceai_openai import OpenAIInstrumentor
   from openai import OpenAI
   

2. Register and initialize the tracking service:
   Early in your code's execution, callregisterFunctions. You need to specify a project name for the project you're working on and set the project type toOBSERVE(Observation). This will return atrace_providerObject.

   # 确保环境变量已设置
   # 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"
   trace_provider = register(
   project_type=ProjectType.OBSERVE,
   project_name="my-first-openai-project", # 给你的项目起个名字
   )
   

3. injection tracker:
   Next, thetrace_providerinjected into the LLM client you're using.Future AGI provides a specializedInstrumentorThe

   OpenAIInstrumentor().instrument(tracer_provider=trace_provider)
   

   After executing this line of code, all requests made and responses received through the OpenAI client are automatically tracked and relevant data (e.g. latency, Token consumption, inputs and outputs, etc.) are sent to your Future AGI dashboard.

Step 4: Execute your AI application code

Now you can use the OpenAI client as usual. All API calls are automatically logged.

For example, an example of calling the GPT-4o model for image recognition:

# 创建OpenAI客户端实例
client = OpenAI()
# 调用API
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "这张图片里有什么?"},
{
"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",
},
},
],
},
],
)
# 打印结果
print(response.choices[0].message.content)

Step 5: View and analyze on the Future AGI platform

Once the code is running, log into Future AGI's website and go to the project you created. You will see a visual dashboard with the full link trace information for that API call you just made.

• Tracing Dashboard. You can see detailed information about each LLM interaction, including input prompts, model outputs, functions called, time spent, and cost.
• Failure and Anomaly Detection. The platform automatically flags failed calls or anomalous behavior, and you can set up alerts to notify you when latency is too high, costs are exceeded, or assessment metrics are not being met.
• Custom Evaluation. You can set up automatic assessment rules in the platform. For example, you can create an assessment item to check if a model's answer is polite or if it contains harmful information. The results of these assessments are displayed along with tracking data, giving you more insight into the model's performance.

With these steps, you can start systematically monitoring and improving your AI applications by utilizing Future AGI's powerful observability features.

application scenario

1. AI application development and debugging
   During the development phase, developers need to constantly test and iterate their AI intelligences. future AGI provides an integrated environment for rapid prototyping and systematically comparing the advantages and disadvantages of different cues or model configurations to find the best solution through its powerful evaluation capabilities, greatly reducing debugging and optimization time.
2. Production environment performance monitoring
   When AI applications are deployed in production environments, their performance can be degraded by data drift or the diversity of user inputs. future AGI's "Observe & Protect" feature enables 24/7 real-time monitoring, tracking key business metrics and model quality indicators, and alerting O&M teams as soon as a drop in performance or security risk is detected. As soon as a drop in performance, an increase in hallucinations, or a security risk is detected, an alert is sent to help the operations team intervene before the impact is felt.
3. Compliance and Security for Enterprise AI
   For high-compliance industries such as finance and healthcare, it is critical to ensure that AI output is secure, unbiased, and privacy-neutral. future AGI's Protect module acts as a security fence, filtering harmful or non-compliant output in real-time, while recording all interactions for auditing purposes, to help organizations build trustworthy AI systems.
4. Automated Content Generation and Evaluation
   For teams using AI for content creation (e.g., article summarization, marketing copywriting, code generation), assessing the quality of generated content is a core pain point, and Future AGI supports customized assessment metrics that define "good criteria" in natural language (e.g., whether the summary captures the core idea), thus enabling automated and scaled assessment of the quality of generated content. This enables automated, large-scale assessment of the quality of generated content, eliminating the inefficiency of manual sampling.

QA

1. What type of user is Future AGI Platform for?
   The platform is aimed at AI developers, data scientists, and enterprise technology teams responsible for deploying and maintaining AI applications. Whether you are an individual developer building a rapid prototype or a large enterprise team needing systematic tools to ensure the reliability of AI applications in production environments, you can benefit from it.
2. How does Future AGI's assessment feature differ from other tools?
   One of Future AGI's core strengths is its proprietary assessment technology. Not only does it offer a range of pre-built assessment models to detect hallucinations, toxicity, fidelity, etc., it also allows users to create custom assessment metrics in simple natural language. In addition, its evaluation models are optimized to be cheaper and faster, with accuracy that outperforms general-purpose macromodels such as OpenAI and Gemini on multiple benchmarks.
3. Will accessing Future AGI have a big impact on my application performance?
   No. Future AGI's SDK and integration methods are optimized for performance. For example, the processing latency of its "Protect" safety fence feature is less than 50 milliseconds, with minimal impact on the user experience. Data tracking and telemetry is often done asynchronously, without blocking core application logic.
4. Do I need a large "golden dataset" or manual labeling to use the evaluation function?
   No. A key feature of Future AGI is its "unsupervised" evaluation capability, which pinpoints errors in the output without reference to an answer or "golden data set". The platform also supports the use of synthetic data generation to create diverse test sets, reducing the reliance on manually labeled data.