Building an Agentic HR Front Door on Databricks
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Thursday
I have been exploring one question for the last few months:
What happens when enterprise AI moves from answering questions to actually completing tasks?
That led me to build DwaraOne, an agentic HR front door using Databricks free edition.
Most HR assistants today can explain a policy or answer a question.
But an employee usually wants more than an answer.
For example, when someone asks for leave, the agent should be able to check the balance, understand team availability, identify possible conflicts, reference the right policy, submit the request, route it to the correct approver, and track what happens next.
That is the shift I wanted to explore.
I built the current preview using Databricks Free Edition, which made the exercise even more interesting.
The architecture includes:
Unity Catalog for governed data access
Silver and Gold layers with Employee 360 views
SQL Warehouse and Delta for analytics and application data
Genie Spaces for employee, manager, and leader experiences
Lakebase for conversation and workflow state
Governed agent tools for actions
Memory across conversations
Audit logging for agent decisions and actions
One of my biggest learnings was that agent architecture should not start with the chatbot.
It should start with the data model, permissions, context, tools, and auditability.
For me, the important loop became:
Ontology → Memory → Tools → Audit
An agent that can act should also be explainable and accountable.
Another interesting lesson was that Free Edition did not feel like a limitation.
It actually pushed me to think more carefully about governance, semantic design, lineage, and architecture before adding more complexity.
The current preview uses synthetic data and supports scenarios like leave requests, policy guidance, onboarding, offboarding, workforce analytics, manager insights, consent tracking, and agent health monitoring.
I am still improving it and would really value feedback from the Databricks community.
What would you add to this architecture?
And what is the first enterprise task you would trust an agent to complete?
Preview: dwaraone.online
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Thursday
Hello,
This is a great exploration of where enterprise AI is heading. The focus on moving beyond chat-based assistance toward governed action-taking agents is especially important. I like the emphasis on ontology, memory, tools, and auditability because trust will be the biggest factor in enterprise adoption.
Best Regards
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Friday
Thanks for your feedback! @wiliam65jenny
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Sunday
Interesting to see the shift from AI assistants that answer questions to agents that can actually complete tasks. For enterprise adoption, I’m curious how teams are thinking about measuring business impact here — is the focus more on reducing manual HR workflows, improving employee experience, or increasing the accuracy and speed of decisions?