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

From Gross sales Dilemma to Information-Pushed Motion

Even the most effective industrial provides are solely as efficient as their supply. At Databricks, we offer free credit score provides to assist prospects 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 process may be opaque and shortly flip right into a time-consuming, multi-team effort, particularly when accounts are unexpectedly ineligible for provides. Gross sales groups typically must dig via 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 prospects with high-value provides. Even when accounts are recognized to be eligible, it’s not at all times apparent which needs to be prioritized.

Constructing a Smarter System with Agent Bricks

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

• Rapidly determine which buyer accounts qualify for credit score provides
• Perceive the precise cause an account isn’t eligible
• Rank eligible accounts to concentrate on the highest-impact prospects first

As an intern in Enterprise Technique and Operations this summer time, I had a brief turnaround time, so pace and ease had been crucial. Agent Bricks let me shortly construct a high-quality answer 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 below 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 answer makes use of three brokers: two AI/BI Genie brokers and a Information Assistant agent, managed by a supervisor to orchestrate duties and knowledge circulation:

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

From Guide Requests to Self-Serve Insights

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

• Self-serve eligibility troubleshooting: As a substitute of routing via a number of groups and Slack threads, gross sales groups can now shortly perceive supply eligibility points and take knowledgeable motion straight, because of built-in explanations
• Extra clever focusing on: Gross sales groups can concentrate on high-value accounts primarily based on actual progress indicators and supply relevance, not hunches, streamlining how they determine high-impact alternatives
• Quicker outreach: By growing supply understandability and decreasing handbook friction, the response SLA decreases from 48 hours to below 5 seconds, and gross sales groups can transfer extra shortly and confidently

Most significantly, the system scales as accounts are added and extra provides are created. Buyer account and GTM insights replace routinely when the reference information in Unity Catalog modifications, and new supply packages 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 straight share context, sure items of data wanted to be duplicated throughout them, regardless that the supervisor “is aware of all.” For instance, the Account Prioritization agent’s information desk features a column indicating which supply – if any – every account is eligible for (already recognized to the Eligibility agent). It additionally incorporates context in regards to the utilization eligibility bands for every supply sort (already recognized to the Supply Particulars agent). This duplication ensures the Prioritization agent can cause about focusing on and rank accounts appropriately.
• Person 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 straight into their on a regular basis workflows.

Conclusion

Industrial provides solely work if gross sales groups know who to focus on — and why. Earlier than Agent Bricks, this was a handbook, multi-team problem that slowed down outreach and launched ambiguity into our packages. With Agent Bricks, we had been capable of construct, check, and refine a multi-agent AI system with nothing extra in hand than our information 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 day by day, the positive factors have already been significant; it’s made supply focusing on quicker, extra clear, and extra scalable. The actual magic lies within the prioritization of accounts: the system routinely aggregates buyer information and supply data to intelligently floor the highest-impact alternatives first, and I didn’t even have to inform the agent precisely find out how to do it. Now that’s information intelligence. 
 

Get began constructing with Agent Bricks and create your first answer immediately.