Salesforce Deepens Observability in Agentforce 360

Salesforce today launched several agent observability tools, bringing deeper visibility, monitoring, and optimization to every AI agen within the Agentforce 360 Platform.

By providing granular insight into agent behavior, the tools empower organizations to scale AI, continually improve agent performance, and foster better collaboration between humans and intelligent agents.

"As AI adoption accelerates, the biggest enterprise challenge will no longer be about building an organization's first agent; it will become how to best manage a fleet of agents that are making real-world business decisions. You can't scale what you can't see. Agentforce delivers the enterprise-grade operational rigor required for this new agentic world. We're giving IT leaders the critical tools to continuously track performance, debug issues, and prove the ROI of their AI investments, ensuring every intelligent agent performs reliably, securely, and with total transparency," said Adam Evans,, an executive vice president and general manager of Salesforce AI.

The new observability tools within Agentforce span three core areas: analytics, optimization, and health monitoring.

Agent Analytics provides a comprehensive view of how every agent is performing. It enables users to do the following:

• Track agent performance, monitoring usage and effectiveness metrics across all deployed agents in real customer interactions.
• Surface key performance indicator trends over time, to see where performance is improving, declining, or requiring attention.
• Address ineffective topics, actions, or flows, enabling teams to take targeted steps to optimize agent performance.

Agent Optimization gives full observability into every agent interaction, illuminating performance gaps, tracing session flows, and revealing exactly how agents make decisions. It enables users to do the following:

• Access end-to-end visibility of every Agentforce interaction to see exactly how agents respond, step by step, even across complex reasoning chains.
• Cluster and analyze sessions, grouping similar requests to uncover patterns, friction points, and quality trends while scoring agent responses using intent, topic, and quality metrics.
• Optimize agent configuration, identifying configuration issues affecting performance and understanding exactly where tuning, retraining, or guardrails are needed.

Agent Health Monitoring ensures continuous agent uptime, reliability, and responsiveness by providing near-real-time visibility and actionable trust signals on all agents, even during peak loads. It enables users to do the following:

• Monitor agent status continuously, tracking key health metrics in near-real time, ensuring dashboard data is fresh and surfacing potential issues before they impact performance or trust signals.
• Resolve failures proactively with high-speed alerts on critical errors, latency spikes, and escalations.

Deep observability in Agentforce 360 is powered by two foundational building blocks:

1. Session Tracing Data Model that logs every interaction, including user inputs, agent responses, reasoning steps, large language model calls, and guardrail checks, and stores them securely in Data 360. This foundation provides unified visibility and granular, session-level insights to ensure agents are behaving as intended and responding appropriately.
2. MuleSoft Agent Fabric, which provides a single place to register, manage, govern, and observe every agent, regardless of where it was built.

With these new agent observability tools, plus robust governance in MuleSoft and unified context in Data 360, every company can get full end-to-end control of all their agents, all from within the Agentforce 360 Platform.