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Pega AI Tracer: Enterprise‑Grade Observability for Agentic AI
As organizations adopt Agentic AI to automate decisions, orchestrate workflows, and augment human judgement, a critical challenge emerges:
How do we observe, understand, govern, and optimize AI behavior in real time?
Pega AI Tracer addresses this challenge by providing deep, end‑to‑end visibility into AI‑driven executions—turning AI from a black box into a transparent, governable, enterprise‑ready capability.
What Is Pega AI Tracer?
Pega AI Tracer is a native observability and traceability capability that captures the complete execution lifecycle of AI interactions within the Pega Platform.
It enables architects, developers, and business stakeholders to inspect:
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How AI agent’s reason
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Which tools and data sources are used
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How long each step takes
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How many tokens are consumed
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How final responses are derived
All of this information is presented in a single, coherent execution trace.
Token‑Level Transparency
One of the most impactful capabilities visible in AI Tracer is token‑level attribution.
For every execution, AI Tracer shows:
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Total tokens consumed
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Tokens per agent
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Tokens per tool call
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Tokens per data retrieval
This makes AI costs measurable, explainable, and optimizable.
Why this matters
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Architects can design cost‑efficient agent flows
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Teams can tune prompts and payload sizes
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Organizations gain predictability over AI spend
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Cost trade‑offs become transparent instead of assumed
Steps to launch AI Tracer:
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Configure the Agent(s) in the Self or Web Portal Landing Pages.
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Launch the Self Service or Web Portal
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Launch the Conversation agent and click on 3 dots
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Launch AI tracer
Data Source and Tool LineageData Source and Tool Lineage
AI Tracer clearly highlights tool and data source usage as first‑class execution steps.
For each tool call, the trace captures:
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Tool name
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Input payload
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Output payload
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Processing time
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Tokens consumed
This provides:
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Data lineage for AI decisions
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Clear visibility into external integrations
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Support for audit and compliance reviews
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Confidence that AI is using approved data sources only
Performance and Latency Insights
Each AI execution in Pega AI Tracer includes:
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Total processing time
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Duration per agent
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Duration per tool
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Duration per LLM call
This enables teams to:
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Identify bottlenecks quickly
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Compare alternative orchestration approaches
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Balance response quality versus latency
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Set realistic SLAs for AI‑driven experiences
AI behavior becomes engineerable, not experimental.
From Black Box AI to Glass Box AI
Traditional generative AI experiences typically provide only:
“Here is the output.”
Pega AI Tracer provides:
“Here is how the output was produced, step by step.”
This represents a shift to glass‑box AI, where:
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Reasoning is inspectable
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Behavior is predictable
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Results can be explained
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Systems can be trusted
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Why Pega AI Tracer Matters for the Enterprise
Based on what is visible in the screenshots, the enterprise value of AI Tracer is clear:
Governance and Compliance
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Full execution trace
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Clear auditability of AI decisions
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Transparent data usage
Cost Control
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Token‑level visibility
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Measurable AI economics
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Optimization based on real usage
Debugging and Optimization
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Faster root‑cause analysis
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Clear performance bottlenecks
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Confident tuning of agent flows
Trust and Adoption
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Explainable outcomes
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Predictable AI behavior
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Greater confidence from business and IT stakeholders
Conclusion
Pega AI Tracer is not just a diagnostic tool.
It is a core platform capability that enables organizations to build responsible, scalable, and enterprise‑grade Agentic AI solutions.
By combining observability, explainability, cost transparency, and governance, Pega AI Tracer ensures that AI in the enterprise is not only powerful—but also trustworthy and controllable.
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