Salesforce has introduced a new suite of monitoring tools aimed at solving one of the biggest blind spots in corporate AI, understanding how autonomous agents make decisions once deployed in real customer environments. The new platform, Agentforce Observability, gives businesses a window into the decision making processes of their AI agents, offering near real time insights into how prompts, data signals and internal logic drive interactions.

The launch comes as companies increasingly deploy AI agents to handle customer support, sales, workflow automation and backend operations. While adoption is accelerating, many organisations admit they struggle to understand why their AI systems respond the way they do. That opacity has raised concerns about accuracy, safety and compliance, especially in industries where AI driven decisions require regulatory oversight.

Agentforce Observability attempts to close that gap by exposing the reasoning traces behind each action an agent takes. Salesforce says the tool can reveal the agent’s internal steps, policy checks, data sources and intermediate conclusions, allowing teams to debug behaviour, audit responses and correct failures before they escalate.

It works alongside Agentforce, Salesforce’s platform for building autonomous AI agents for CRM and enterprise tasks. With observability added, companies can watch an agent’s chain of thought in a controlled and privacy safe format, monitor performance anomalies and detect when agents stray from approved instructions.

Industry analysts say this type of transparency is becoming essential as AI agents transition from experimental tools to core operational systems. Businesses want automation, but they also want guardrails. Observability gives executives and engineering teams a way to evaluate how an AI model reached a specific outcome, whether the reasoning was appropriate and what data it relied on.

Salesforce executives argue the feature will help enterprises adopt AI with more confidence, especially as companies expand their use of autonomous agents for sensitive processes. They emphasise that the system is built with governance controls to ensure that exposed reasoning is used for safety, not to reverse engineer proprietary models.

The launch also reflects a broader shift in the enterprise AI market, where transparency, auditability and traceability are moving from optional features to competitive requirements. As AI agents take on more autonomy, the question is no longer whether they can perform tasks, but whether organisations can trust the pathways they use to reach their conclusions.

Salesforce’s move signals that the next phase of AI adoption will be defined as much by oversight as by automation. Companies do not just want AI that performs well, they want AI they can watch, measure and correct. Agentforce Observability builds directly into that demand, offering a blueprint for how future enterprise AI systems may be governed.