# AI Agent Observability

Specialized engineers deliver production-ready agent observability that improves reliability, performance, and cost.

### Premier-Certified Partner Expertise

[Aws](/content/partners/aws-factored/index.html) [Snowflake](/content/partners/snowflake-factored/index.html)   [Databricks](/content/partners/databricks-factored/index.html) [Astronomer](/content/partners/astronomer-factored/index.html) [Dbt Labs](/content/partners/dbt-labs-factored/index.html) [Google Cloud](/content/partners/google-cloud-factored/index.html) [Airbyte](/content/partners/airbyte-factored/index.html) [Azure](/content/partners/azure-factored/index.html)

## Your Agents Are Running. But Are They Working?

### AI agents can fail anywhere across reasoning, retrieval, tool selection, execution, and output generation. Without end-to-end observability, teams struggle to answer three basic questions:

**Why** did it fail? Identify reasoning, model, prompt, tool, and data failures.

**Where** did it fail? Trace every step from user input through final output.

**How** do we fix it? Turn failure data into targeted, measurable improvements.

## When Is AAO Right for You

### **Failures Are Hard to Diagnose**

Your agents fail, but your team can't consistently determine where or why.

### **Performance Is Inconsistent**

Similar requests produce different results, and you don't have a reliable baseline for improvement.

### **Agent Costs Continue To Grow**

Token usage, model selection, and reasoning depth are increasing costs without clear controls.

### **You Need Production Ownership**

You need monitoring, alerts, playbooks, and internal processes your team can operate after delivery.

Phase 01 - Observability & Attribution  
Phase 02 - Root Cause & Optimization  
Phase 03 - Monitoring & Ownership

### Make every agent execution fully traceable.

Instrument the full agent lifecycle to expose reasoning paths, tool interactions, failure points, and baseline performance.

**Deliverables | Full-Stack Agent Traceability:** A production-ready observability layer that traces every agent execution across inputs, reasoning, tool calls, responses, and outputs.

### Turn failure signals into targeted system improvements.

Attribute errors to prompts, models, reasoning, tools, or data, then optimize the components actually driving failure.

**Deliverables | Root-Cause Intelligence + Optimized Agents**: A quantified failure model, targeted system optimizations, and a remediation framework for improving agent reliability and consistency.

### Operationalize agent performance at production scale.

Deploy monitoring, regression alerts, cost controls, and operational playbooks that keep agents reliable and your team in control.

**Deliverables | Production Monitoring + Operational Control:** Production dashboards, regression alerts, cost-control levers, and operational playbooks your team can own and run independently.

## Proven At Enterprise Scale.

### **+20% Agent Quality**

Through targeted model optimization.

### **3 Mo. To Production**

From instrumentation through optimization, monitoring, and handover.

### **100% Traced Lifecycle**

Inputs, reasoning, tool calls, responses, and outputs instrumented.

## Built Around Your Agent Stack

### LangGraph  
### LangChain  
### PydanticAI  
### Langfuse  
### LangSmith  
### OpenTelemetry  
### Custom Frameworks

## FAQs

**What is Agentic Observability & Optimization?**  
**Who is this service for?**  
**Do we need to replace our existing agent stack?**  
**How long does the engagement take?**  
**Who works with our team?**  
**What will we own at the end?**  
**How quickly can we start?**

**Know Why Your Agents Fail.**

**Then Fix It.**

Own the monitoring and playbooks after handover.