01
Multi-Source Connectivity
Connect to SQL databases, NoSQL stores, REST APIs, file systems, and vector databases. Deep Agent queries them all to find answers.
← The Workbench
DEEP AGENT
A research agent with RAG built in.
Use a multi-step agent for questions that require investigation across approved sources rather than one prompt and one answer. Review the planned work, queries, intermediate results, and synthesis.
Built for deep questions, not simple ones.
SEPARATE & SPECIALIZED
A simple question may need one query. A decision question may require several sources, tested hypotheses, and a record of how the answer was assembled.
Deep Agent is separate from Chat Studio and AI Lab. Connect approved sources, configure retrieval, and use the agent to plan and execute a reviewable investigation.
From question to a reviewable investigation.
Ask, "Why did customer churn increase last quarter?" Deep Agent can plan an investigation such as:
- query churn data by segment, region, and product;
- compare it with support-ticket volume;
- review NPS scores over time;
- check for pricing or product changes;
- compare approved competitive data; and
- synthesize the findings while preserving intermediate work for review.
The quality of the result depends on source access, data quality, model choice, and human review. Add Zentinelle AI separately when the workflow requires runtime policy and evidence.
Your dedicated AI research environment.
- Independent data connections · Connect sources directly to Deep Agent, separate from Lab datasets.
- RAG pipelines · Use retrieval-augmented generation for document-heavy research and analysis.
- Multi-step reasoning · Break complex questions into sub-queries and synthesize the results.
- Knowledge management · Build persistent knowledge bases that accumulate research over time.
- Deep research · Support literature review, competitive analysis, and due-diligence investigation.
- Optional control layer · Connect Zentinelle AI separately when the workflow requires agent-level policy, enforcement, and evidence.
Deep Agent Capabilities
02
Iterative Analysis
Deep Agent doesn’t stop at the first answer. It validates findings, tests alternatives, and refines conclusions through multiple iterations.
03
Visualization Generation
Generate charts and graphs from intermediate results, then review the code, data, and presentation before using the output.
The questions Deep Agent was built for.
Root cause analysis
“Why did conversion rates drop last month?” Deep Agent investigates across marketing data, product analytics, support tickets, and competitive intelligence to identify contributing factors.
Customer intelligence
“Which enterprise accounts are at risk?” Deep Agent analyzes usage patterns, support interactions, billing history, and engagement metrics to identify warning signs.
Market research
“How does our pricing compare to competitors, and what's the impact on win rates?” Deep Agent correlates pricing data with sales outcomes and competitive intelligence.
Operational analysis
“Where are the bottlenecks in our fulfillment process?” Deep Agent examines order data, inventory levels, shipping times, and complaints to find the constraints.
Not generic AI. Not a shallow chat.
Compared with generic chat: Deep Agent connects to sources approved for the Workbench environment instead of requiring a user to paste all context into one conversation.
Compared with a single-query interface: Deep Agent can plan multiple steps, correlate sources, test alternatives, synthesize findings, and expose intermediate work for review.
