Challenge
Great customer support depends on fast, accurate answers but the knowledge needed to give this is rarely in a single place. Every day, Salesforce Support Engineers for MuleSoft had to manually check over 12 separate sources to answer a single customer question — from knowledge articles, GitHub, Slack, Confluence, docs, and more. Complex cases could take up to a couple of hours to track down the right information, before an engineer could even start solving the customer's problem.
Objectives
The team set out to build an AI system that could:
- Instantly search across scattered knowledge sources at once
- Route each question to the right source automatically, instead of relying on engineers to guess
- Deliver accurate, trustworthy answers engineers could act on with confidence
Solution
Using Agent Fabric, the team quickly turned each knowledge source into an MCP server, giving AI agents a standard way to query them directly. This allows engineers to use Agent Fabric for case research, live customer chats, finding docs and known issues, and troubleshooting. By building an Agent Broker across the workflow, they created a smart dispatcher that reads each question, routes it to the right source or specialized agent, and returns one clear answer with links back to the original material. Built-in governance keeps customer data safe, and engineers can reach Hive right where they already work, in Slack or their coding tools.
The results
<
1
min
wait time for support down from hours
30
%
increase in cases resolved per engineer daily
~
80
hours
saved per day globally on querying
15
%
forecasted drop in time to resolve cases
Agent Fabric turned hours of searching across systems and record into seconds - enabling a secure multi-agent workflow to work at scale. Our support engineers can now spend their time solving problems for customers, not hunting for information.
Sean LiSr Manager, Technical Support Engineering, Salesforce
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