From Gross sales Dilemma to Information-Pushed Motion
Even the very best industrial presents are solely as efficient as their supply. At Databricks, we offer free credit score presents 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 as if an easy process will be opaque and rapidly flip right into a time-consuming, multi-team effort, particularly when accounts are unexpectedly ineligible for presents. Gross sales groups typically have to dig by way 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 way in which of offering prospects with high-value presents. 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 deal with the issue, our crew 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 steerage on to gross sales groups. In lower than two days, I created a device that lets gross sales reps:
- Rapidly establish which buyer accounts qualify for credit score presents
- 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 season, I had a brief turnaround time, so velocity and ease had been crucial. Agent Bricks let me rapidly 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 Data Assistant agent, managed by a supervisor to orchestrate duties and data movement:
1. Provide Particulars Agent utilizing Data Assistant
This agent is educated on our unstructured inside supply documentation (PDFs, slide decks) to deeply perceive supply guidelines, eligibility necessities, and the supply outreach and supply course of. Since Data Assistant can take paperwork of their present kind, I didn’t must do any further work to parse, chunk, or embed this data.
2. Provide Eligibility Agent utilizing AI/BI Genie
This agent analyzes structured buyer account information, ruled in Unity Catalog, to find out which prospects qualify for particular presents and, simply as importantly, why others don’t. The agent can floor the particular eligibility requirement(s) that an account doesn’t meet and recommend follow-up steps if a gross sales rep desires to troubleshoot this additional. To assist the agent stroll by way of the eligibility course of, the info desk contains columns related to every of the eligibility standards.
3. Account Prioritization Agent utilizing AI/BI Genie
This agent appears to be like at structured GTM information to rank eligible accounts utilizing utilization information, progress alerts, and supply relevance. Gross sales groups get a transparent, prioritized record of who to contact first.
Without having to analysis supervisor agent structure or have interaction with technical groups, I used to be in a position to construct a useful AI agent system instantly 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 an alternative of routing by way of a number of groups and Slack threads, gross sales groups can now rapidly perceive supply eligibility points and take knowledgeable motion instantly, due to built-in explanations
- Extra clever focusing on: Gross sales groups can concentrate on high-value accounts based mostly on actual progress alerts and supply relevance, not hunches, streamlining how they establish high-impact alternatives
- Quicker outreach: By growing supply understandability and decreasing guide friction, the response SLA decreases from 48 hours to below 5 seconds, and gross sales groups can transfer extra rapidly and confidently
Most significantly, the system scales as accounts are added and extra presents are created. Buyer account and GTM insights replace mechanically when the reference information in Unity Catalog adjustments, and new supply applications will 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 couple of limitations to notice:
- Agent Overlap: As a result of the brokers can’t instantly 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 information desk features a column indicating which provide – if any – every account is eligible for (already recognized to the Eligibility agent). It additionally accommodates context in regards to the utilization eligibility bands for every supply kind (already recognized to the Provide 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 method as a Slackbot or into Salesforce would put eligibility particulars and steerage instantly into their on a regular basis workflows.
Conclusion
Business presents 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 information and our objective.
Although our system has a couple 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 focusing on quicker, extra clear, and extra scalable. The actual magic lies within the prioritization of accounts: the system mechanically 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 how you can do it. Now that’s information intelligence.
Get began constructing with Agent Bricks and create your first answer right this moment.
