Automatan captures the complete execution of enterprise AI systems—making models, prompts, retrieval, tool calls, agent trajectories, and business outcomes visible in one operational layer.
End-to-end agent tracing · Root-cause intelligence · Production AI control
As AI moves from experimentation into business-critical operations, runtime visibility becomes the foundation for reliability, governance, and continuous improvement.
An AI-native operations layer that continuously captures and connects agent traces, model calls, prompts, retrieved context, tool interactions, workflow states, and outcomes.
Application monitoring exposes uptime, latency, errors, and infrastructure health. It cannot explain why an agent selected a source, invoked a tool, followed a decision path, or produced an incorrect outcome.
Automatan reconstructs complete agent trajectories, correlates behavior across components, identifies failure origins, and turns runtime telemetry into actionable system intelligence.
Each stage transforms fragmented AI telemetry into operational understanding, diagnostic precision, and system control.
Capture user inputs, prompts, model calls, retrieved context, tool usage, memory access, intermediate decisions, workflow states, and final outputs.
Connect spans and events into a chronological view of how the agent interpreted the objective, selected knowledge, invoked tools, and progressed through the workflow.
Track latency, token usage, model behavior, retrieval performance, tool reliability, workflow completion, human intervention, and downstream outcomes.
Determine whether a failure originated in the model, prompt, context, retrieval pipeline, tool invocation, agent logic, or multi-agent workflow.
Use trace intelligence to refine prompts, change retrieval strategies, compare models, correct tool selection, reduce latency, control cost, and redesign workflows.
Six capabilities create one operational intelligence layer across the complete enterprise AI stack.
Inspect complete agent trajectories, intermediate decisions, model calls, tool interactions, workflow branches, and final outcomes.
See what information was retrieved, how it was ranked, which sources influenced the response, and where knowledge gaps occurred.
Compare model behavior, prompt versions, response patterns, token consumption, latency, and cost across environments.
Track tool selection, input parameters, execution status, returned results, retries, and downstream impact.
Monitor task progression, multi-agent coordination, handoffs, loops, completion states, and failure points.
Trace approvals, policy checks, human interventions, restricted actions, and operational boundaries across every agent execution.
Automatan keeps prompts, context, retrieval, reasoning steps, tools, decisions, and outcomes connected so teams can understand not only what happened, but where and why it happened.
Connect model calls, retrieval events, tool interactions, and workflow states within one complete execution path.
Trace incorrect or incomplete outcomes back to the specific component, span, decision, or dependency that caused them.
Identify which models, prompts, retrieval steps, tools, and workflow branches contribute most to runtime and cost.
Surface recurring errors, behavioral changes, retrieval degradation, tool instability, and emerging production failure patterns.
Preserve the prompts, evidence, actions, approvals, and system conditions that influenced each agent outcome.
Every execution becomes an inspectable operational object connecting the observed outcome to its underlying cause.
The agent retrieved the correct refund policy, selected the order-verification tool, and received a valid eligibility result.
The workflow applied an outdated escalation instruction stored in agent memory after the tool call completed.
Memory precedence overrode the current policy retrieved during the active session.
Update context precedence rules, invalidate the outdated memory entry, and evaluate affected production traces for the same failure pattern.
TLS 1.3, AES-256, RBAC, SSO/SAML.
Multi-region architecture and SLA-backed uptime.
Zero-retention default and no training on customer data.
SaaS, VPC, on-prem, APIs, and enterprise SDKs.