Agent Bricks in Motion: Smarter Supply Concentrating on for Gross sales Groups

From Gross sales Dilemma to Knowledge-Pushed Motion

Even the very best industrial provides are solely as efficient as their supply. At Databricks, we offer free credit score provides to assist clients get began or speed up adoption, however gross sales representatives face a deceptively easy query: which of my buyer accounts are eligible, and which ought to I attain out to first?

What looks like an easy activity may be opaque and rapidly flip right into a time-consuming, multi-team effort, particularly when accounts are unexpectedly ineligible for provides. Gross sales groups usually have to dig by documentation, seek the advice of Slack threads, and manually examine accounts with operations groups. This creates pointless back-and-forth, slows down momentum, and will get in the way in which of offering clients with high-value provides. Even when accounts are recognized to be eligible, it’s not at all times apparent which must be prioritized.

Constructing a Smarter System with Agent Bricks

To sort out the issue, our crew turned to Agent Bricks — Databricks’ platform for constructing high-quality AI brokers on enterprise knowledge — and constructed a multi-agent system that delivers clear, actionable steering on to gross sales groups. In lower than two days, I created a instrument that lets gross sales reps:

• Shortly determine which buyer accounts qualify for credit score provides
• Perceive the precise purpose an account isn’t eligible
• Rank eligible accounts to deal with the highest-impact prospects first

As an intern in Enterprise Technique and Operations this summer season, I had a brief turnaround time, so pace and ease have been essential. Agent Bricks let me rapidly construct a high-quality resolution and supply the enablement gross sales groups wanted.

Designing the Multi-Agent Resolution

Utilizing Agent Bricks’ Multi-Agent Supervisor, I designed a system that chains collectively three purpose-built brokers beneath one supervisor. Like an air-traffic controller, the Supervisor decides which agent to delegate every a part of the query to after which stitches their responses into one clear reply.

One Supervisor, Three Specialised Brokers

My resolution makes use of three brokers: two AI/BI Genie brokers and a Data Assistant agent, managed by a supervisor to orchestrate duties and data circulation:

With no need to analysis supervisor agent structure or have interaction with technical groups, I used to be capable of construct a practical AI agent system immediately on our buyer knowledge and supply program paperwork.

From Guide Requests to Self-Serve Insights

The multi-agent resolution removes guesswork and creates a seamless, explainable expertise. By combining structured buyer knowledge with unstructured supply program data, the system permits:

• Self-serve eligibility troubleshooting: As a substitute of routing by a number of groups and Slack threads, gross sales groups can now rapidly perceive supply eligibility points and take knowledgeable motion immediately, because of built-in explanations
• Extra clever concentrating on: Gross sales groups can deal with high-value accounts based mostly on actual progress alerts and supply relevance, not hunches, streamlining how they determine high-impact alternatives
• Sooner outreach: By growing supply understandability and decreasing guide friction, the response SLA decreases from 48 hours to beneath 5 seconds, and gross sales groups can transfer extra rapidly and confidently

Most significantly, the system scales as accounts are added and extra provides are created. Buyer account and GTM insights replace mechanically when the reference knowledge in Unity Catalog adjustments, and new supply applications may be supported by updating the paperwork within the data base – with no new code required.

Limitations

Whereas the present system is highly effective, there are a number of limitations to notice:

• Agent Overlap: As a result of the brokers can’t immediately share context, sure items of knowledge wanted to be duplicated throughout them, regardless that the supervisor “is aware of all.” For instance, the Account Prioritization agent’s knowledge desk features a column indicating which supply – if any – every account is eligible for (already recognized to the Eligibility agent). It additionally comprises context concerning the utilization eligibility bands for every supply kind (already recognized to the Supply Particulars agent). This duplication ensures the Prioritization agent can purpose about concentrating on and rank accounts accurately.
• Consumer Workflow Integration: Most gross sales groups work primarily in Slack and Salesforce, not Databricks. Integrating this technique as a Slackbot or into Salesforce would put eligibility particulars and steering immediately into their on a regular basis workflows.

Conclusion

Business provides solely work if gross sales groups know who to focus on — and why. Earlier than Agent Bricks, this was a guide, multi-team problem that slowed down outreach and launched ambiguity into our applications. With Agent Bricks, we have been capable of construct, check, and refine a multi-agent AI system with nothing extra in hand than our knowledge and our purpose.

Although our system has a number of limitations in its present kind and isn’t embedded within the instruments gross sales groups use day by day, the features have already been significant; it’s made supply concentrating on quicker, extra clear, and extra scalable. The actual magic lies within the prioritization of accounts: the system mechanically aggregates buyer knowledge and supply data to intelligently floor the highest-impact alternatives first, and I didn’t even have to inform the agent precisely methods to do it. Now that’s knowledge intelligence. 
 

Get began constructing with Agent Bricks and create your first resolution at this time.