Full-Stack Observability for Agentic AI
Track complex reasoning, tool use, and autonomous decisions in production. ABV provides end-to-end visibility into agentic workflows that traditional solutions can't capture.
- Predictable execution paths
- Rule-based workflows
- Fixed behavior
- Database queries
- Network requests
- Compute metrics
- Storage I/O
- Non-deterministic outputs
- LLM reasoning loops
- Dynamic tool selection
- Multi-step reasoning traces
- Tool call sequences
- Token usage & costs
- Traditional infrastructure Logs
Harden AI Agents Pre-Deployment
Validate AI agents defenses with automated adversarial testing
Run comprehensive attack simulations to identify and fix vulnerabilities in your AI agents before they reach production.
Gain aggregated and granular visibility to understand system performance and behavior across every layer
- Application
- Session
- Agent
- Trace
- Span
Support for Any Agentic Framework
Seamlessly integrate ABV with OpenTelemetry, LangGraph, Bedrock, Strands Agents, ADK, or your custom agentic stack.
The OpenTelemetry (OTEL) integration enables teams to preserve their AI pipeline and be compatible across systems
FAQ About Agentic Observability
Agentic observability tracks the complete decision-making process of AI systems that make autonomous choices. Unlike traditional monitoring that logs inputs and outputs, agentic observability captures reasoning chains, tool selections, multi-step workflows, and the context behind each decision. This includes tracing LLM calls, function invocations, retrieval operations, and how agents coordinate with each other.
Traditional applications follow deterministic, pre-programmed logic — given the same input, they always produce the same output along predictable paths. Agentic systems use LLMs to make context-aware decisions, meaning the same input can produce different outputs based on reasoning, available tools, or environmental factors. This non-deterministic behavior requires specialized observability to understand why agents made specific choices.
Single-agent systems use one AI model to complete tasks sequentially or delegate to tools. Multi-agent systems coordinate multiple specialized agents — each with different capabilities, knowledge bases, or roles — that collaborate, negotiate, or compete to solve complex problems. Multi-agent architectures require observability that tracks inter-agent communication, task handoffs, and distributed decision-making.
Agentic behaviors include:
- Dynamic tool selection: Agent chooses between calculator, web search, or database query based on the question
- Multi-step reasoning: Agent plans a sequence ("First search documentation, then test the code, then format results")
- Self-correction: Agent detects errors in its output and retries with different approaches
- Adaptive retrieval: Agent decides when it needs more context and queries relevant knowledge bases
- Goal decomposition: Agent breaks complex requests into subtasks and orchestrates their execution
Agentic RAG gives AI systems the ability to decide when, where, and how to retrieve information — rather than always searching a fixed knowledge base. The agent evaluates whether it needs external context, selects appropriate data sources (vector databases, APIs, search engines), formulates queries dynamically, and determines if retrieved information is sufficient or if it needs to search again. This contrasts with traditional RAG, which retrieves documents for every query using predefined rules.
