I use JARVIS to build content creation agents that create web page and other relevant content based on highly technical Physician Researcher discussions of clinical trial data.
Agent workflows have transformed clinical content creation and streamline complex data querying
What is our primary use case?
How has it helped my organization?
JARVIS is a game changer. It allows my team to streamline the effective querying of curated data in my DynamoDB via an MCP Server connection, to create highly technical reliable outputs. The agentic platform is excellent for streamlining workflows and rapidly building efficiencies from agentic collaboration. This also allows for the effective use of data which would overwhelm the input tokens of most off-the-shelf LLM tools.
What is most valuable?
The ability to create agents with specific querying of my DynamoDB tables is incredibly efficient. Once an effective agent has been created, I just 'rinse and repeat.' This has added an entirely different scale to my organization's output in a very technical and precise space.
What needs improvement?
A Visual Agentic Workflow similar to N8N or Make.com would be helpful for some agent operators.
For how long have I used the solution?
I have used the solution for 4 months.
Which solution did I use previously and why did I switch?
I have used Google Workflow, Make.com, and N8N, and the agentic creation with JARVIS is perhaps the most effective, especially when synced with my custom MCP server.
What's my experience with pricing, setup cost, and licensing?
I find the pricing model to be very fair. With the advancements in AI workflows from Microsoft and Google, one can streamline their agentic needs with JARVIS by querying from a custom AWS Agentcore MCP Server and utilizing Google Workspace integration for various final outputs like NotebookLM with Google.
Which other solutions did I evaluate?
I have considered alternate solutions like Claude Desktop and Scout.
What other advice do I have?
I recommend building a knowledge base for your organization quickly regarding the effective data query structure for the various tables deployed in your MCP server. Rank and file users should spend more time using current agents versus creating agents. I would recommend building agents with JARVIS prioritized based on replacing inefficient current workflows. It is best to deploy with a set of proven agents and allow your team to iterate from there.
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