Salesforce has announced its new observability tools for Agentforce 360.
This comes after its annual report revealed that AI implementation had increased by 282% since 2024.
These tools enable enterprises to deploy AI agents without worrying about the reliability and safety of their performance within a system.
Salesforce’s observability tools provide AI agents with the capabilities to analyze performance, optimize interactions, and monetize stability.
Agent Analytics
This capability allows enterprises to view how well an AI agent is operating through monitoring its movements, how it’s improving/declining, and where these pain points are coming from.
This can be turned into performance data, trends, and insights to understand how efficiently these agents are performing and take actionable steps to improve their usage.
This can also be done across all implemented agents, allowing enterprises to view their agents’ overall effectiveness on customer interaction and support their continuous improvement.
Agent Optimization
As a key observable capability, Optimization offers customer enterprises full transparency with each agent interaction.
Customers can uncover how agents make decisions and what led them to make those choices, highlighting performance gaps and session flows to diagnose any issues and deduce the steps needed to improve its performance.
This can include prompt, rule, or data source adjustments to solve misinterpreted information, inconsistent results or agent hesitation.
Salesforce provides access to end-to-end visibility for customers to view each agent’s response and action, even with larger, complicated action chains.
For less varied issues, similar requests can be accumulated to uncover larger problems in patterns or trends.
Customers can also identify an agent’s configuration issues to pinpoint how an agent’s behaviour is affecting its operation and uncover which areas need to be retrained or personalized further for improved performance.
Agent Health Monitoring
This capability can monitor an AI agent’s reliability and safety level to ensure that it is running as expected.
It provides almost real-time visibility and alerts when the agent is performing unpredictably, notifying the company before any significant damage takes hold.
It measures an agent’s ability to handle requests, time taken to respond, and tracks incidents such as failures, breaks in activity, or invalid responses.
By leveraging the capability, teams can speedily detect and resolve issues to minimize agent downtime and continue productivity.
This tool is formed by two of Agentforce’s components, acting as the foundation for the observability tool by supplying the data and governance structure needed to monitor agents:
- Session Tracing Data Model: By logging every agent interaction, the data model can store all its data in Data 360 and provide the observability tool the means to generate reliable analytics, error identifiers, and support optimization for unified visibility.
- MuleSoft Agent Fabric: This enables enterprises to control, register, and review agents to justify how they function and interact.
AI Implementation Report
In a report published in November, Salesforce announced that AI implementations had increased to 282% since last year.
This data reveals that companies are now at a far better position to deploy pilot projects at scale rather than risk the threat of experimentation.
Despite this, data governance, security, and trust remain high priorities, requiring risk management across workflows.
This means that more companies are going to require higher visibility and control across large-scale AI deployments, which is where Salesforce’s observability tools come in.
By supporting enterprises with agent interactions, Salesforce’s observability tools can decrease operational risk by allowing teams to keep up to date with agent visibility and analytics to keep agent deployments stable.
Reddit, a customer of Salesforce, highlighted how Salesforce has allowed the customer enterprise to scale agents securely through consistent visibility.
John Thompson, VP of Sales Strategy and Operations at Reddit, stated: “By observing every Agentforce interaction, we can understand exactly how our AI navigates advertisers through even the most complex tools.
“This insight helps us understand not just whether issues are resolved, but how decisions are made along the way.
“Observability gives us the confidence to scale these agents, continuously monitor performance, and make improvements as we learn from their interactions.”
