Analytics for the agentic automation era

Analytics for every
agent and bot.

Measure ROI, monitor reliability, govern risk, and unify data across AI agents, RPA bots, workflow automations, and autonomous business processes.

Updated

Live · Automation Estate
Last 30 days
Total Value Delivered
$2.4M
+18% vs prior period
Agent Success Rate
94.2%
+2.1% vs prior period
Workflows / hr
1,247
+8% vs prior period
Open Exceptions
12
-34% vs prior period
ROI TrendMonthly · $K
JanDec
Agent Health
Procurement
97%
Finance
94%
HR Onboarding
88%
Customer Ops
91%
👤
Human Handoff Rate
3.1%
↓ from 4.8%
Avg Workflow Latency
142ms
P95: 380ms
🔗
Platforms Connected
8
2 pending
🛡
Active Risk Alerts
2
3 resolved today

Automation is becoming agentic.
Visibility has not caught up.

Enterprises now run a mix of AI agents, RPA bots, workflow automations, custom scripts, APIs, orchestration tools, and human approvals. Traditional dashboards show siloed technical logs, not business value, operational reliability, or governance risk.

Fragmented visibility across tools and platforms

Bots, agents, APIs, and orchestration tools each produce siloed data that never adds up to a coherent picture.

No clear view of ROI or business outcomes

Activity metrics don't translate into cost savings, capacity gains, or revenue increases.

Hard to monitor agent reliability

AI agents make decisions, call tools, and hand off work. Traditional monitoring wasn't built for this.

Exceptions and handoffs buried across systems

Failures, retries, and human escalations live in logs no one reads until something breaks.

Governance gaps widen with autonomy

As agents take on more decisions, ownership, audit trails, and policy adherence become harder to track.

Integration data scattered across logs and APIs

Connecting signals from a dozen platforms requires custom engineering that never stays current.

Leaders lack one source of truth

No single view of automation performance, ROI, risk, and coverage means decisions are made blind.

One analytics layer for the
agentic automation stack.

AutomationAnalytics.ai is a unified intelligence layer for measuring, monitoring, governing, and connecting automation across the enterprise.

ROI and value realization analytics

Connect bot runs, agent actions, and workflow completions to cost savings, FTE capacity released, and measurable business outcomes.

Agent and bot observability

Monitor performance, success rates, latency, tool calls, exceptions, and reliability across every automation type.

Workflow reliability tracking

Track completion rates, failure hotspots, retry loops, SLA breaches, and operational bottlenecks end to end.

Exception and human handoff monitoring

Surface escalations, approval checkpoints, human interventions, and override patterns across all workflows.

Governance and risk signals

Track ownership, policy adherence, audit trails, control failures, and risk events across autonomous workflows.

Cross-platform integration intelligence

Unify automation data from AI-agent platforms, RPA tools, workflow engines, APIs, and enterprise systems.

Automation portfolio view

Understand the health, coverage, value, and risk profile of your full automation estate in one place.

Business outcome tracking

Map automation activity to process-level outcomes, department targets, and enterprise KPIs.

One platform. Four questions answered.

ROI

What business value are we creating?

Connect automation activity to cost savings, productivity gains, SLA improvements, capacity released, revenue protected, and measurable outcomes.

Explore ROI
Observability

What is happening across our automation estate?

Monitor agents, bots, workflows, APIs, tool calls, latency, failures, retries, handoffs, and performance across the full automation lifecycle.

Explore Observability
Governance

Can we scale autonomy safely?

Track ownership, controls, policy adherence, audit trails, risk events, approvals, escalation paths, and human-in-the-loop oversight.

Explore Governance
Integration

Can we connect the platforms behind our automation ecosystem?

Unify signals from AI-agent platforms, RPA suites, workflow engines, iPaaS tools, logs, APIs, enterprise applications, and data platforms.

Explore Integration

Built for the agentic AI era.

From bots that follow rules to agents that make decisions. New analytics must track outcomes, decisions, interventions, failure modes, data movement, and business impact — not just uptime.

Traditional analytics
  • Bot runs
  • Success / failure
  • Queue status
  • Processing time
  • Basic exception logs
  • Siloed platform reports
Agentic analytics
  • Goal completion
  • Tool-call performance
  • Reasoning path visibility
  • Human intervention rate
  • Outcome quality
  • Policy and governance adherence
  • ROI by workflow
  • Risk and escalation signals
  • Cross-platform integration health
  • Business impact tracking

Who it is for.

Automation CoEs

One view of portfolio performance, ROI, and operational health across every automation type and platform.

AI Transformation Teams

Measure the impact of AI-agent deployments and multi-agent workflows as they move into production.

CIO & Technology Leaders

A single source of truth for automation value, coverage, risk, and strategic priorities.

Operations Leaders

Monitor reliability, exceptions, handoffs, and process outcomes across shared services and back-office operations.

Shared Services Teams

Track automation adoption, quality, and business impact across finance, HR, procurement, and customer operations.

AI Agent Platform Companies

Demonstrate measurable ROI, reliability, and governance to enterprise buyers evaluating agentic platforms.

Automation Consultants & SIs

Deliver analytics-backed evidence of automation value to clients and build differentiated service offerings.

GRC Teams

Ensure bots and agents operate within policy, maintain audit trails, and support compliance reporting.

Metrics that matter.

Purpose-built measures for every dimension of automation performance.

ROI
Hours saved
Cost avoided
FTE capacity released
ROI by process
Payback period
SLA improvement
Revenue protected
Productivity gain
Observability
Agent success rate
Bot success rate
Workflow completion rate
Tool-call failure rate
Exception rate
Retry rate
Human handoff rate
Workflow latency
Governance
Policy violations
Approval checkpoints
Risk events
Audit trail completeness
Escalation events
Control failures
Human override rate
Owner coverage
Integration
Connected platforms
Data freshness
API health
Event ingestion volume
Failed integrations
Source system coverage
Log coverage
Workflow dependency mapping

Ready to measure the next generation of automation?

Build one source of truth for ROI, observability, governance, and integration across every bot, agent, and workflow.

Request access