Data Agents

Your data holds the answers you need —if you can reach them.

But traditional exploration workflows bury insight under fragile queries, disconnected tools, and slow manual analysis.

Exploration shouldn’t feel like excavation. It should feel like conversation — governed, optimized, and built for scale.

If you’re stuck with:

You’re moving slower than you have to — and leaving insights buried.

SQL queries you tweak endlessly just to find simple patterns

Notebooks you manually update, rerun, and patch to follow a new idea

Blind spots because your tooling can’t surface multi-layered relationships on demand

Features

Live, Intelligent Data Interaction

Connect directly to SQL, NoSQL, and vector stores with fine-tuned connectors and dynamic schema discovery
Ask natural language questions; receive optimized, explainable queries mapped to real datasets
Surface joins, aggregations, and latent patterns in seconds — no manual stitching

Notebook-Integrated Discovery

Conversational agents embedded into Jupyter, Colab, and VSCode notebooks
Auto-generate experiments, reparameterize models, visualize outputs dynamically
Refactor experimental workflows on demand without breaking reproducibility guarantees

Governed Access and Auditability

Build RBAC-bound capabilities and scoped credential passing
Automated agent scaling: one agent, one cluster, or one thousand — fully orchestrated
Real-time tracing, failover recovery, and progressive rollout strategies built in

Calliope Agents are already reshaping how teams build multi-agent workflows, RAG systems, automated research assistants, and autonomous dev pipelines

— all while preserving operational control and security. Because building real AI systems demands more than brittle scripts and blind trust — it demands engineering you can count on.

Explore the Building Blocks