Summary
Precisely and Databricks present an architecture for deploying agentic AI on trusted enterprise data, using data classification, address verification, iterative address fixing, enrichment, spatial analytics, and governed orchestration. In the telecommunications example, customer, network-coverage, property, and location data are combined into a source-of-truth table so AI agents can answer operational questions and diagnose customer-service issues, such as why a subscriber receives lower broadband speeds than expected. The design emphasizes data quality, metadata, confidence scores, governance, audit logs, retry and recovery logic, and observability, although the presentation demonstrates a proposed solution rather than reporting independently validated production metrics or measured business outcomes.
In Production
Four LLMOps case studies like this one, in your inbox every Tuesday and Thursday.
- Industry
- Telecommunications
On this page
Overview
Precisely and Databricks describe a telecommunications-oriented agentic AI architecture built around a trusted data layer rather than around model selection alone. The central use case is to help a telecommunications support organization answer customer questions and diagnose service problems by combining customer records with validated addresses, network coverage, property characteristics, spatial data, and other enriched attributes. The system is intended to turn a natural-language request into a sequence of data-quality, verification, enrichment, and analytical operations, with an orchestration agent coordinating specialized agents inside a governed Databricks environment.
The demonstrated scenario involves a customer who reports receiving approximately 100 Mbps despite a promised speed of 700 Mbps. The proposed agents classify the customer’s message, retrieve and verify the account and address, apply location and property enrichment, compare the customer’s position with 5G and LTE coverage, and explain that different rooms or parts of the property may fall into different coverage areas. The system can then suggest operational alternatives such as moving equipment, swapping equipment, applying a credit, or considering a nearby fiber connection. The presentation positions this approach as a way to reduce technician back-and-forth and improve customer experience, but it does not provide measured production accuracy, latency, cost, resolution-rate, or customer-satisfaction results. The benefits should therefore be treated as an architectural proposal and demonstration rather than as independently validated outcomes.
Problem and Use Case
The underlying problem is that enterprise AI applications often have access to large volumes of data without having reliable, complete, contextual, and continuously monitored information. Precisely’s framing is that an LLM or agent can only produce useful operational answers when the underlying data has been cleaned, standardized, related, and enriched. For a telecommunications provider, relevant information can be distributed across customer systems, address databases, network-coverage sources, property records, flat files, cloud storage, and geospatial files. A language model cannot safely infer the correct answer from these sources without an integration and quality layer that establishes what the fields mean and whether they are fit for use.
The example source-of-truth table contains telecommunications service information and hundreds of appended attributes. The demonstration refers to approximately 700 attributes in one example and describes the possibility of appending roughly 10,000 attributes to an address. These attributes can include service type, coverage, property type, construction type, building details, coordinates, building identifiers, and other location-related characteristics. The objective is not to place all of these fields into every prompt, but to make them available to governed agents that can select relevant information for a specific question.
Data and Foundation Architecture
The proposed pipeline starts with heterogeneous inputs such as cloud storage, flat files, and shape files. Data is classified before specialized processing is applied. An NER-based classification component identifies names, telephone numbers, email addresses, organizations, addresses, locations, and noise in unstructured or semi-structured input. The classified elements are then routed to appropriate Precisely capabilities, including name parsing, email verification, address verification, geocoding, and noise filtering.
A quality-check stage evaluates the resulting records. When an address or other field does not meet the required quality threshold, an address-fixer agent can attempt additional processing. The resulting records receive a confidence or quality indication that can support acceptance, rejection, or human review. This is an important LLMOps control: the system is not described as blindly accepting generated output. Instead, confidence boundaries are intended to determine whether an agent’s result is sufficiently reliable for downstream use.
After validation, the pipeline enriches records with Precisely data and customer-owned data. The enrichment layer can combine a telecommunications provider’s proprietary coverage information with external or Precisely-provided location and property attributes. In the example, coverage data is used alongside property characteristics to identify whether a customer is in a 5G, 4G, LTE, or legacy coverage area and to investigate how building structure, floor, room, exterior walls, or equipment placement may affect service. The resulting source-of-truth layer acts as business context for downstream agents rather than as a replacement for the provider’s operational systems.
Agents and Orchestration
Three principal agent capabilities are presented. The data-classification agent accepts natural language and labels relevant entities and fields. The address-fixer agent uses Precisely address, geocoding, verification, data-quality, and noise-filtering APIs through a Model Context Protocol, or MCP, layer. MCP is used here as a way to expose API capabilities and operating rules to an LLM with structured context. The enrichment agent selects and combines relevant customer and Precisely attributes to support a specific business question.
The address fixer is designed as an iterative reasoning chain rather than a single API call. If the first verification or geocoding attempt produces an unsatisfactory result, the LLM can identify potential noise or problematic components, adjust the input or configuration, and invoke the Precisely services again. An illustrative address containing a care-of phrase is used to show how the system might remove or reinterpret a non-address component before retrying. The process continues until it reaches a satisfactory result or an acceptance boundary is triggered. This pattern can improve resilience to messy input, but it also introduces risks of repeated calls, higher latency, variable cost, and over-processing. In a production implementation, retry limits, deterministic normalization rules, escalation paths, and monitoring would be important safeguards.
The customer-support workflow uses a multi-agent orchestration layer. The customer’s message is classified first, after which the system verifies the phone number and address, retrieves account-related information, applies spatial enrichment, and analyzes the relationship between the customer’s property and available network services. The orchestration agent then assembles an explanation and possible next steps. Databricks Genie is also shown answering natural-language questions against the prepared data, such as identifying how many 5G customers live in multi-story buildings and breaking results down by construction type. This suggests two related interaction modes: analytical self-service over governed tables and an operational support agent that coordinates multiple tools and data services.
Production LLMOps Controls
The architecture places governance within Databricks. The presentation emphasizes that customer data should remain within a controlled enterprise environment and claims that the approach prevents customer data from being used to train an external AI system. More generally, the design is intended to provide access control, governance, and auditability for agent and data operations. The stated controls include audit logs, retry and recovery logic, and observability for batch processing and data changes.
A shared metadata foundation is described as spanning cataloging, data quality, enrichment, and related capabilities. This metadata layer is important for agentic systems because agents need more than raw values: they need descriptions of fields, relationships, business rules, provenance, and acceptable usage. Policy and access controls are presented as guardrails, while the data foundation supplies cleaned, contextualized, and monitored records. The architecture is also described as interoperable with existing stacks and capable of running across cloud, on-premises, and hybrid environments, reducing the need for a complete rip-and-replace migration.
From an LLMOps perspective, the most relevant operational boundary is between probabilistic reasoning and deterministic enterprise services. The LLM interprets natural-language requests, chooses or coordinates actions, and can decide when an address needs another pass. Precisely APIs and data-quality services perform specialized validation and enrichment. Confidence tiers, logs, retry behavior, and governed execution provide mechanisms for inspecting the system’s behavior. A mature deployment would still need explicit evaluation datasets, tool-call success measurements, hallucination and grounding tests, latency and cost budgets, prompt and model versioning, human-review rates, and rollback procedures; those measurements are not supplied in the case description.
Results and Tradeoffs
The demonstrated result is an end-to-end explanation of a network-service problem that combines customer context with spatial and property data. Instead of returning a generic troubleshooting script, the agent is shown identifying a likely mismatch between expected and observed service, explaining the relationship between the customer’s office, living room, equipment, and coverage areas, and recommending possible remedies. It can also investigate nearby fiber availability and rule out other explanations, such as a recent hail storm, when the available data does not support them.
The principal strength of the approach is its emphasis on grounding agent behavior in enterprise data and reusable tools. It makes data quality, address resolution, enrichment, and spatial reasoning explicit components of the AI system. It also recognizes that a useful agent needs operational controls, not merely a capable model or a well-written prompt. The main tradeoffs are architectural complexity and reliance on the quality, freshness, permissions, and coverage of the underlying data. Iterative multi-agent reasoning can increase tool-call volume and make behavior harder to predict. Enriched attributes may be incomplete or stale, and a confident explanation can still be wrong if the source-of-truth table or network-coverage data is inaccurate. Consequently, the architecture should be evaluated with representative customer cases, negative and ambiguous examples, controlled access to sensitive data, and clear human escalation rules before it is used for autonomous customer decisions.