Progress AI Observability

Product Introduction

1. Definition: Progress AI Observability is a specialized observability and monitoring platform designed for production-grade AI agents, LLM applications, and RAG (Retrieval-Augmented Generation) systems. It falls under the technical categories of AI Operations (AIOps), LLM observability, and agent debugging.
2. Core Value Proposition: It exists to provide developers and engineering teams with end-to-end visibility into the non-deterministic execution of AI workflows. Its primary value is enabling rapid debugging of agent failures, granular cost attribution for LLM usage, and systematic evaluation of output quality—addressing critical gaps left by traditional application performance monitoring (APM) and log management tools.

Main Features

1. AI Agent Tracing and Execution Path Visualization: This feature captures the complete, graph-like execution trace of an AI agent run. It logs every step, including prompts sent, model calls (to providers like OpenAI, Anthropic, or Azure OpenAI), tool/function invocations, retrieval steps from vector databases, retries, errors, and final outputs. How it works: The platform uses lightweight SDKs for .NET, Python, and JavaScript that instrument popular AI frameworks (Semantic Kernel, LangChain, LlamaIndex) to automatically collect span data, latency, and token counts, presenting them in a unified timeline view for analysis.
2. LLM Cost and Token Usage Analytics: This feature provides real-time and historical analysis of AI spending tied directly to specific workflows, agents, and models. How it works: By intercepting API calls to LLM providers, the platform tracks input/output token consumption per request, calculates estimated costs based on provider pricing, and aggregates this data. It allows teams to attribute costs to specific features, identify expensive patterns like agent loops or inefficient prompts, and forecast spend.
3. LLM-as-a-Judge Evaluation and Quality Scorecards: This feature enables automated, scalable evaluation of AI output quality using another LLM as an evaluator ("judge"). How it works: Teams define evaluation criteria (e.g., groundedness, relevance, safety, helpfulness). The system runs these evaluator models against captured production traces, generating quality scores and verdicts. This allows for side-by-side comparison of prompt versions, model performance, and tracking of quality metrics over time, moving beyond simple latency and error rate monitoring.

Problems Solved

1. Pain Point: The "black box" nature of AI agent workflows makes root-cause analysis of failures extremely time-consuming. Traditional error logs show an application crash but not why an agent hallucinated, skipped a critical tool, or entered an infinite loop.
2. Target Audience: The primary personas are AI/ML Engineers and Developers building agentic systems; Engineering Leaders and DevOps Managers responsible for the reliability, performance, and cost of production AI applications; and Enterprise Architects needing governance, audit trails, and data residency for scaled AI deployments.
3. Use Cases: Essential scenarios include: debugging a customer-facing chatbot that provides ungrounded answers; optimizing a multi-agent workflow that has unexpectedly high token costs; conducting a post-release evaluation to compare the quality of outputs from GPT-4 vs. Claude 3; and meeting compliance requirements by maintaining an audit trail of all AI agent decisions and data sources.

Unique Advantages

1. Differentiation: Unlike general-purpose APM tools (Datadog, New Relic) or Python-centric AI dev tools (LangSmith), Progress AI Observability offers native, first-class support for the .NET ecosystem, including deep integration with Semantic Kernel and Microsoft's AI stack. It is built for enterprise production environments from the ground up, with features like SSO, data residency options (BYOS), and governance controls that many newer, developer-focused tools lack.
2. Key Innovation: Its core innovation is the correlation of cost, quality, and trace data within a single execution context. It doesn't just show that a request was expensive or that an output was poor—it shows the exact prompt, the specific retrieval step that provided bad context, and the subsequent model call that consumed excess tokens, all in one visualized trace. This closed-loop feedback is critical for iterative AI improvement.

Frequently Asked Questions (FAQ)

1. How does AI observability differ from traditional application monitoring? Traditional monitoring tracks infrastructure metrics (CPU, memory), application logs, and HTTP request latency. AI observability focuses on the unique components of LLM workflows: prompt and response content, token usage per model call, the sequence and success of tool executions, retrieval effectiveness, and LLM-evaluated output quality, which are invisible to standard APM.
2. Does instrumenting my AI agent with an observability SDK impact its performance or latency? The SDKs are designed to be lightweight and perform data collection asynchronously. Instrumentation adds negligible overhead and does not block the synchronous execution path of your agent, ensuring no meaningful impact on user-facing latency or workflow performance.
3. Can I use Progress AI Observability to improve my prompts and workflows, not just debug them? Absolutely. The platform is built for the full AI development lifecycle. By analyzing production traces, you can identify common failure patterns, use real-world data to create evaluation datasets, run experiments comparing different prompts or models, and validate improvements before full deployment, turning observability data into a direct driver of AI quality.
4. What data is collected, and how is sensitive or PII data handled? The platform captures trace data including prompts, responses, metadata, and performance metrics. It provides controls for teams to redact, exclude, or limit the logging of sensitive data fields at the SDK level, ensuring compliance with internal data governance policies.
5. Is this tool suitable for teams not using the Microsoft or .NET stack? Yes. While it offers superior integration for .NET and Azure AI services, it provides full-featured SDKs for Python and JavaScript/TypeScript and supports all major AI frameworks (LangChain, LlamaIndex) and LLM providers (OpenAI, Anthropic, open-source models), making it a viable choice for any polyglot or Python-focused engineering team.