Solution blueprints.
Not products.
These are practitioner-designed agentic solution concepts for real enterprise problems. We design the approach, the architecture, and the logic. You own the implementation. No platform lock-in — just clear thinking on how AI agents can drive actual outcomes.
Why most agentic NL-to-data deployments fail
The problem is structural. Asking an LLM to figure out your data model is like asking someone who has never seen your warehouse to write the audit. It produces something that looks right.
LLMs don't know your schema
An LLM has never seen your FACT_OPPORTUNITY table, how it joins to DIM_ACCOUNT, or what "pipeline" means in your org. It has no inherent understanding of your data model — it can only work with what you give it in context. And it is trained to produce fluent output, not to guarantee correctness.
Naive NL-to-SQL fails at scale
Ask the LLM to simultaneously manage multi-turn context, detect intent, search for the right table, follow a repeatable approach, and generate the query — and you get non-determinism. The same question yields different SQL on different runs. Enterprise reporting requires consistency.
Most vendor solutions take shortcuts
The standard pattern: dump raw tables into the context and hope the LLM figures it out. No intent detection. No semantic views. No knowledge catalog. No healing when the query fails. Databricks Genie, Snowflake Cortex, custom RAG solutions — all fall short for the same structural reason.
Talk2Data in action — natural language query returning governed, structured results from Snowflake
The knowledge-first framework
Three pillars underpin the architecture. Every agentic NL-to-data solution is built on all three — in sequence.
Storage
Where and in what format data lives. Raw source objects and curated dimensional models coexist — each accessible through the knowledge layer, never directly.
Retrieval
How agents discover and access the right data. A knowledge catalog maps every view to intent, sample questions, and schema. The agent picks the right view before generating a single line of SQL.
Enrichment
Derived metrics, pre-aggregations, hierarchies, and business labels built on curated data. Consistent calculations that match how the business already reports — not recomputed ad hoc.
Six-layer solution architecture
All natural language queries flow through the Knowledge and Retrieval layer only. Consumers never access raw or curated tables directly — governance and semantics are enforced at the layer boundary.
All consumers query through the Knowledge layer only — no direct base-table access
Single entry point for all natural language data requests; intent-to-view mapping
Agent picks the right path based on intent and catalog
Raw and curated data; views and enrichment sit on top — no storage duplication
Out-of-band from query path — agent never writes, only reads via Knowledge layer
What makes it work in production
Each capability exists because a naive approach failed on a live engagement. These are not features — they are lessons encoded as architecture.
Intent detection
Classify every question (pipeline_summary, win_rate, list_opportunities) before touching the database. The agent knows which view to use before generating SQL.
Knowledge catalog
A curated store of view metadata, business descriptions, sample questions, and intent mappings. The agent looks here first — not at raw table schemas.
Semantic views
Pre-built views that encode business logic and grain. The LLM adds filters and simple aggregations — it does not invent joins over hundreds of base tables.
Multi-turn context
Entities, filters, and prior answers carry forward across conversation turns. No amnesia between questions. No re-stating context every time.
Self-healing queries
When execution fails, the healer analyses the error, applies deterministic repair (fix the column name, add the missing table), and retries — with the same knowledge context.
Domain packs
Modular, domain-specific knowledge for CRM, Finance, and Incident Management. Each pack ships with KBQs, KPI definitions, semantic views, and test cases.
How a query actually flows
Not one big prompt hoping for the best. Seven discrete steps, each with a clear responsibility.
User asks in natural language
Intent detection classifies the question
Catalog lookup selects the right semantic view
SQL generated against the known view schema
Snowflake executes with row-level security applied
Healer checks result — retries if needed
Answer returned with context for follow-up
Domains in production
Domain packs ship with pre-built KBQs, KPI definitions, semantic views, and test cases. Not starting from scratch — starting from validated patterns.
Salesforce CRM
- Pipeline by stage and quarter
- Open opportunities by rep
- Win rate by lead source
- Account health signals
SAP Finance
- GL balances and cost centre spend
- AP/AR ageing
- Budget vs actuals
- Intercompany reconciliation
IT Incident Management
- Open incidents by priority
- MTTR by team and category
- Recurring issue patterns
- SLA breach risk
Extensible
- Any structured data source
- Custom domain packs
- Multi-source federation
- Bring your own semantic layer
Ready to talk to your data?
We design the knowledge layer, the semantic views, the catalog, and the agentic workflow. You get an agentic AI solution that works in production — not just in a demo.
Talk to us about your use caseai@agentadda.in