AI Observability: Full-Stack Monitoring for Modern AI Systems

AI observability gives engineering, data, and compliance teams end-to-end visibility into how AI models behave in production—across infrastructure, orchestration layers, large language models (LLMs), agents, and user interaction.

If you’re building or buying AI products, you need to go beyond logging. You need verifiable insights into what your models are doing, how tools and retrieval pipelines are behaving, where costs are creeping, and whether your outputs are safe, accurate, and compliant.

Swept makes AI observability simple, actionable, and scalable.

What Is AI Observability?

AI observability is the structured monitoring and traceability of AI systems across every layer—from the raw prompt and response, to semantic search and infrastructure performance, to end-user interaction and feedback.

Unlike traditional monitoring, AI observability focuses on questions like:

• What was the exact prompt, context, and model used?
• Why did the model make a certain decision?
• Was the output grounded in retrieved knowledge?
• Did the model hallucinate or drift from policy?
• Which agentic tools were called—and in what order?
• How do token counts, latency, and cost evolve over time?

Layers of AI Observability

Application/UI Layer

User inputs, feedback buttons, latency, satisfaction signals

Orchestration Layer

Prompt chains, tool calls, retry logic, branching paths

Agentic Layer

Multi-step plans, memory use, goal progression, reasoning traces

LLM & Model Layer

Prompts, completions, token usage, latency, hallucination risk

Semantic Search (RAG)

Embedding versions, retrieval relevance, latency, grounding coverage

Infrastructure Layer

GPU/CPU load, network throughput, endpoint uptime, cost metrics

Swept tracks every layer automatically—without changing your codebase.

Why AI Observability Matters

AI isn’t a black box—it’s just missing the right instrumentation.

With proper observability, you gain:

• Model accountability: Know what was said, why, and when—down to the model version.
• Risk monitoring: Detect hallucinations, tool failures, latency spikes, and grounding errors.
• Performance optimization: Analyze cost per query, completion speeds, and retrieval quality.
• Compliance-readiness: Map decision traces and outputs for audits, buyers, or regulatory review.
• Continuous improvement: Use structured feedback and test coverage to fine-tune your agents.

AI Observability With Swept

Swept is a full-stack observability platform purpose-built for AI and agentic systems. We make it easy to:

• Monitor LLM usage, orchestration, and tool calls without custom logging
• Visualize agent behavior, token spend, and grounding drift in real time
• Flag hallucinations, retries, broken chains, or retrieval mismatches
• Generate verifiable “trust packets” for buyers, regulators, or your own team
  

Whether you’re using OpenAI, Anthropic, Azure AI, or running open-source models, Swept fits your stack.

AI Observability for Healthcare, Finance, and Regulated AI

Swept is trusted by AI vendors and buyers in high-risk sectors like:

• Healthcare: Ensure safe diagnoses, patient explanations, and retrieval-based answers
• Finance: Monitor agent compliance with regulatory guardrails and audit readiness
• Legal: Trace decisions made in LLM-driven drafting, summarization, or search
• Research: Benchmark models, detect drift, and verify data provenance
  

We help teams move beyond “vibes”—and prove their AI works.

AI Observability FAQs