Prove AI is developing an observability-first foundation designed for production generative AI systems. Their mission is to enable engineering teams to understand, diagnose, and remediate failures within complex AI pipelines, including LLM inference, retrieval processes, and agent orchestration.
The current release, v0.1, provides an opinionated observability pipeline specifically for generative AI workloads through:
- A containerized, OpenTelemetry-based telemetry pipeline.
- Preconfigured collection of traces, metrics, and logs tailored for AI systems.
- Instrumentation patterns for RAG pipelines, embeddings, LLM inference, and agent-based systems.
- Compatibility with standard backends like Prometheus.
The current release, v0.1, provides an opinionated observability pipeline specifically for generative AI workloads through:
- A containerized, OpenTelemetry-based telemetry pipeline.
- Preconfigured collection of traces, metrics, and logs tailored for AI systems.
- Instrumentation patterns for RAG pipelines, embeddings, LLM inference, and agent-based systems.
- Compatibility with standard backends like Prometheus.
A Python package designed to provide production-ready templates for Generative AI agents on Google Cloud. It allows developers to focus on agent logic by automating the surrounding infrastructure, including CI/CD pipelines, observability, security, and deployment via Cloud Run or Agent Engine.
Key features and offerings include:
- Pre-built agent templates such as ReAct, RAG (Retrieval-Augmented Generation), multi-agent systems, and real-time multimodal agents using Gemini.
- Automated CI/CD integration with Google Cloud Build and GitHub Actions.
- Data pipelines for RAG using Terraform, supporting Vertex AI Search and Vector Search.
- Support for various frameworks including Google's Agent Development Kit (ADK) and LangGraph.
- Integration with the Gemini CLI for architectural guidance directly in the terminal.
Key features and offerings include:
- Pre-built agent templates such as ReAct, RAG (Retrieval-Augmented Generation), multi-agent systems, and real-time multimodal agents using Gemini.
- Automated CI/CD integration with Google Cloud Build and GitHub Actions.
- Data pipelines for RAG using Terraform, supporting Vertex AI Search and Vector Search.
- Support for various frameworks including Google's Agent Development Kit (ADK) and LangGraph.
- Integration with the Gemini CLI for architectural guidance directly in the terminal.
Infinite Monitor is an AI-powered dashboard builder that allows users to describe the widget they want in plain English, and an AI agent will write, build, and deploy it in real time. Each widget is a full React app running in an isolated iframe, offering flexibility and customization. Users can drag, resize, and organize these widgets on an infinite canvas for various applications like cybersecurity, OSINT, trading, and prediction markets.
The project supports multiple AI providers and offers features like dashboard awareness, live web search, and a widget marketplace. It prioritizes security with local-first storage and threat scanning.
The project supports multiple AI providers and offers features like dashboard awareness, live web search, and a widget marketplace. It prioritizes security with local-first storage and threat scanning.
"Prove AI is a self-hosted solution designed to accelerate GenAI performance monitoring. It allows AI engineers to capture, customize, and monitor GenAI metrics on their own terms, without vendor lock-in. Built on OpenTelemetry, Prove AI connects to existing OpenTelemetry pipelines and surfaces meaningful metrics quickly.
Key features include a unified web-based interface for consolidating performance metrics like token throughput, latency distributions, and service health. It enables faster debugging, improved time-to-metric, and better measurement of GenAI ROI. The platform is open-source, free to deploy, and offers full control over telemetry data."
Key features include a unified web-based interface for consolidating performance metrics like token throughput, latency distributions, and service health. It enables faster debugging, improved time-to-metric, and better measurement of GenAI ROI. The platform is open-source, free to deploy, and offers full control over telemetry data."
This article details building end-to-end observability for LLM applications using FastAPI and OpenTelemetry. It emphasizes a code-first approach, manually designing traces, spans, and semantic attributes to capture the full lifecycle of LLM-powered requests. The guide advocates for a structured approach to tracing RAG workflows, focusing on clear span boundaries, safe metadata capture (hashing prompts/responses), token usage tracking, and integration with observability backends like Jaeger, Grafana Tempo, or specialized LLM platforms. It highlights the importance of understanding LLM behavior beyond traditional infrastructure metrics.
This article explores the emerging category of AI-powered operations agents, comparing AI DevOps engineers and AI SRE agents, how cloud providers are responding, and what engineers should consider when evaluating these tools.
Logs, metrics, and traces aren't enough. AI apps require visibility into prompts and completions to track everything from security risks to hallucinations.
Nemo Agent Toolkit simplifies building production-ready LLM applications by providing tools for creating, managing, and deploying agents. It offers features like memory management, tool usage, and observability, making it easier to integrate LLMs into real-world applications.
Cisco and Splunk have introduced the Cisco Time Series Model, a univariate zero shot time series foundation model designed for observability and security metrics. It is released as an open weight checkpoint on Hugging Face.
* **Multiresolution data is common:** The model handles data where fine-grained (e.g., 1-minute) and coarse-grained (e.g., hourly) data coexist, a typical pattern in observability platforms where older data is often aggregated.
* **Long context windows are needed:** It's built to leverage longer historical data (up to 16384 points) than many existing time series models, improving forecasting accuracy.
* **Zero-shot forecasting is desired:** The model aims to provide accurate forecasts *without* requiring task-specific fine-tuning, making it readily applicable to a variety of time series datasets.
* **Quantile forecasting is important:** It predicts not just the mean forecast but also a range of quantiles (0.1 to 0.9), providing a measure of uncertainty.
* **Multiresolution data is common:** The model handles data where fine-grained (e.g., 1-minute) and coarse-grained (e.g., hourly) data coexist, a typical pattern in observability platforms where older data is often aggregated.
* **Long context windows are needed:** It's built to leverage longer historical data (up to 16384 points) than many existing time series models, improving forecasting accuracy.
* **Zero-shot forecasting is desired:** The model aims to provide accurate forecasts *without* requiring task-specific fine-tuning, making it readily applicable to a variety of time series datasets.
* **Quantile forecasting is important:** It predicts not just the mean forecast but also a range of quantiles (0.1 to 0.9), providing a measure of uncertainty.
This article details the steps to move a Large Language Model (LLM) from a prototype to a production-ready system, covering aspects like observability, evaluation, cost management, and scalability.
