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

From Gross sales Dilemma to Knowledge-Pushed Motion

Even the very best business affords are solely as efficient as their supply. At Databricks, we offer free credit score affords 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 a simple activity may be opaque and shortly flip right into a time-consuming, multi-team effort, particularly when accounts are unexpectedly ineligible for affords. Gross sales groups typically must dig by means of 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 best way of offering clients with high-value affords. Even when accounts are recognized to be eligible, it’s not at all times apparent which ought to be prioritized.

Constructing a Smarter System with Agent Bricks

To sort out the issue, our workforce 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 software that lets gross sales reps:

• Rapidly determine which buyer accounts qualify for credit score affords
• 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 time, I had a brief turnaround time, so velocity and ease had been important. Agent Bricks let me shortly 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 Information Assistant agent, managed by a supervisor to orchestrate duties and knowledge circulate:

With no need to analysis supervisor agent structure or interact with technical groups, I used to be in a position to construct a purposeful AI agent system immediately on our buyer knowledge and provide 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 provide program data, the system permits:

• Self-serve eligibility troubleshooting: As a substitute of routing by means of a number of groups and Slack threads, gross sales groups can now shortly perceive provide 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 primarily based on actual development alerts and provide relevance, not hunches, streamlining how they determine high-impact alternatives
• Sooner outreach: By rising provide understandability and lowering guide friction, the response SLA decreases from 48 hours to beneath 5 seconds, and gross sales groups can transfer extra shortly and confidently

Most significantly, the system scales as accounts are added and extra affords are created. Buyer account and GTM insights replace routinely when the reference knowledge in Unity Catalog modifications, and new provide applications may be supported by updating the paperwork within the information 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 data wanted to be duplicated throughout them, although the supervisor “is aware of all.” For instance, the Account Prioritization agent’s knowledge desk features a column indicating which provide – if any – every account is eligible for (already recognized to the Eligibility agent). It additionally comprises context concerning the utilization eligibility bands for every provide kind (already recognized to the Provide Particulars agent). This duplication ensures the Prioritization agent can purpose about concentrating on and rank accounts appropriately.
• Consumer Workflow Integration: Most gross sales groups work primarily in Slack and Salesforce, not Databricks. Integrating this method as a Slackbot or into Salesforce would put eligibility particulars and steering immediately into their on a regular basis workflows.

Conclusion

Business affords 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 had been in a position to construct, take a look at, and refine a multi-agent AI system with nothing extra in hand than our knowledge and our aim.

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

Get began constructing with Agent Bricks and create your first resolution in the present day.