BASF is a German multinational and one of many world’s largest chemical corporations, recognized for its built-in Verbund manufacturing community, world scale, and broad portfolio spanning from primary chemical compounds to superior agricultural options. With its sturdy basis in analysis and improvement, BASF operates throughout various industries whereas repeatedly driving innovation and sustainability.
Certainly one of its key operational divisions is BASF Coatings, which focuses on growing, manufacturing, and advertising superior automotive and industrial coatings, together with ornamental paints. As a pioneer in eco-efficient floor applied sciences, BASF Coatings can also be on the forefront of digital transformation, leveraging AI-powered platforms to reinforce productiveness, innovation, reliability, and design.
In partnership with Databricks, BASF Coatings has carried out a production-ready, ruled, and business-impacting multi-agent answer. This strategy not solely enhances cross-team collaboration but in addition allows smarter, sooner decision-making throughout crucial enterprise features — setting a benchmark for the way superior analytics and AI can drive tangible enterprise outcomes.
The Problem: Deliver extra Modularity, Specialization and Management to Agent Methods
As a company with over 11,000 workers throughout greater than 70 websites worldwide, managing the rising complexity and bettering effectivity of cross-department digitalization is a non-trivial job. Extra particularly, turning huge, disparate organizational knowledge into actionable insights, and enabling real-time decision-making and productiveness has turn into the important thing. Fixing this downside mattered as a result of environment friendly digital collaboration and knowledge utilization straight have an effect on market responsiveness, innovation pace, buyer satisfaction, and operational reliability. The stakes had been notably excessive in industries like coatings, the place agility and precision are essential amid quickly altering buyer calls for and sustainability pressures.
An agentic system – the place autonomous or semi-autonomous AI brokers proactively handle enterprise processes and knowledge flows – was the very best answer as a result of it may automate coordination and evaluation duties that beforehand required intensive guide effort. Agent programs may empower organisations like BASF Coatings to:
- Seamlessly combine AI throughout domains, automating routine operations in gross sales, procurement, and provide chain administration.
- Present good, contextual suggestions and automate resolution flows, dramatically lowering bottlenecks and errors.
- Enhance consumer expertise by enabling “always-on” chat assistants for help, Q&A, or workflow integration throughout departments.
- Drive adoption of on a regular basis AI instruments company-wide, making complicated digital capabilities accessible to enterprise stakeholders and fostering knowledge literacy.
As highly effective as an agent might be, as we develop these programs, they may develop extra complicated over time, making them tougher to handle and scale. For instance, an agent can have too many instruments at its disposal and make poor selections about which software to name subsequent, additionally the context grows too complicated for a single agent to maintain monitor of. There’s a want for a number of specialization areas within the system (e.g. supervisor, area orchestration, subject material knowledgeable, and many others.)
One other approach to view the problem is thru the variety of knowledge that varieties the agent system’s information base. Many people are already acquainted with RAG (Retrieval-Augmented Technology), a method that mixes giant language fashions (LLMs) with real-time knowledge retrieval to enhance response accuracy and relevance. Nevertheless, RAG programs are primarily designed to deal with unstructured knowledge – Equivalent to paperwork, net pages, PDFs, or different types of free textual content – relatively than structured tables with predefined fields and relationships. When working with structured knowledge, Textual content-to-SQL is the commonest strategy for pure language analytics. Nevertheless, it typically depends on pre-defined instance SQL queries and lacks built-in mechanisms for knowledge governance and permission management.
The Resolution: An Finish-to-Finish Multi-Agent Supervisor for Structured and Unstructured Data
To deal with these challenges, we suggest breaking our software into a number of smaller, unbiased brokers and composing them right into a multi-agent system. This technique will comply with a supervisor sample that coordinates the specialist brokers – particularly, Genie brokers and function-calling brokers – which work together with the Databricks Vector Retailer Retrieval software.
AI/BI Genie, some of the standard options inside Databricks, is designed to make structured knowledge corresponding to Delta tables and views straight accessible to enterprise customers by leveraging pure language interfaces. It makes use of metadata from Unity Catalog, corresponding to desk descriptions, PK/FK relationships, and column names/descriptions. This metadata guides Genie in parsing consumer questions, establishing correct SQL, and delivering contextually related solutions – serving to to mitigate errors or hallucinations. As well as, Genie authors can improve the area by regionally enhancing metadata, defining joins, including synonyms, and curating BASF-specific directions. This enables knowledge stewards to actively handle and keep the standard of their Genie areas thus contributing on to the agent system with their invaluable enterprise area information.
To ease using Genie inside agent orchestration frameworks, there are frameworks supporting devoted Python wrappers for constructing Genie brokers (verify right here for reference). As well as, Databricks product crew options instance notebooks that stroll our customers via establishing a multi-agent system utilizing Mosaic AI Agent Framework along with Genie. These examples leverage LangGraph (an open-source agent orchestration library) and reveal the best way to compose workflows the place Genie is one specialised agent amongst a number of.
An summary of our structure is as follows. We undertake Databricks’ Mosaic AI framework to simplify the complexities of managing AI agent lifecycles, providing instruments and speedy multi-agent coordination prototyping, rigorous analysis, and efficient real-time operational monitoring. Notably, we additionally combine the deployed supervisor endpoint with Microsoft Groups for real-time agent execution, and make AI-powered insights available to all kinds of customers, together with enterprise stakeholders who’re much less acquainted with knowledge platforms – by embedding conversational deployment endpoints straight inside the Groups interface. Clear, reusable accelerators exist for provisioning cloud assets (Azure Bot Service, App Service) and connecting endpoints to Groups.
Actual Enterprise Influence
Whereas BASF Coatings is growing AI brokers that may improve its enterprise processes, the primary touchdown zone venture, Marketmind, focuses on the Gross sales & Advertising division. The use case allows superior quantitative and qualitative evaluation by consolidating inner Salesforce buyer go to studies and market consumption insights with exterior market traits together with S&P 500 information. A few of this knowledge is already processed and obtainable within the type of Delta tables and views, whereas the remainder exists as free-text recordsdata and PDF paperwork, every arriving at totally different speeds and being up to date at various frequencies. Moreover, the information is managed by totally different groups and stewards. For instance, structured tables are primarily supplied by BASF’s central Enterprise Knowledge Lake (EDL) group, with Gross sales & Advertising enterprise consultants enriching them with domain-specific metadata. In distinction, unstructured knowledge is primarily processed via code-first ETL pipelines developed and maintained by the Coatings Knowledge & AI workplace crew.
Given the complexity of the information panorama, we adopted the multi-agent supervisor structure for the Marketmind venture and used the template pocket book as our start line. We created a Genie area for structured knowledge, enriching it with curated tables, detailed column descriptions, Genie-local be part of relationships, and worth sampling. To enhance accuracy, we added SQL examples and clear directions to information Genie’s responses, and we carried out common Benchmark checks as new knowledge got here in to guage its total efficiency.
For unstructured knowledge corresponding to Salesforce go to studies and market information, we constructed vector search indices for every supply utilizing embeddings to allow context-aware similarity search. We then created Unity Catalog features that wrap Mosaic AI Vector Search queries, making certain enterprise-ready governance, discoverability, and automated MLflow tracing. Lastly, we developed a operate tool-calling agent that invokes vector retrieval instruments to deal with task-specific requests handed alongside by the supervisor.
Our Marketmind venture started its scoping part in April this 12 months, adopted by a 5–6 week proof of idea (PoC). We then moved into the total implementation part, accompanied by technical upskilling workshops, structure critiques, and product and have discussions with the Databricks’ Mosaic AI product crew. We carried out a one‑month pilot with 25 key customers, and are actually within the ultimate refinement stage earlier than go‑stay and rolling out to North America by the tip of October . As soon as launched, greater than 1,000 gross sales representatives worldwide shall be utilizing Marketmind, with inputs up to date continuously.
Marketmind is already altering how BASF Coatings’ gross sales groups put together, have interaction, and comply with up with their clients. As an alternative of attempting to find leads via scattered notes and folders, gross sales representatives obtain customized notifications alongside advised actions and methods based mostly on present occasions available in the market. If additional data is required, Marketmind presents the choice to dig deeper into the underlying knowledge and studies utilizing an easy-to-use chat interface. The screenshot under illustrates this shift. Alerts from the market are offered in an actionable, conversational interface inside Microsoft Groups, so Coating’s gross sales crew can shift their focus from “What occurred?” to “What ought to I do subsequent?” with out switching instruments.
As proven above, gross sales groups cannot solely ask ad-hoc inquiries to the Marketmind chatbot straight in Groups, but in addition obtain proactive adaptive playing cards with the newest market traits on a weekly foundation. Customers can discover subjects of curiosity in higher element by clicking the connected URL, which redirects them to the unique knowledge supply. To additional improve the agent’s high quality, we’ve additionally built-in a voting mechanism that permits customers to shortly give a thumbs up or down, or present extra detailed written suggestions within the backside subject. This suggestions is captured within the mannequin inference desk and built-in with the prevailing payload knowledge.
“Marketmind turns our subject interactions into well timed, AI-driven actions—nudging good follow-ups, surfacing related alternatives, and connecting friends going through related challenges. The consequence: sooner prep, sharper buyer conversations, and extra time promoting the place it counts.” — Adrian Fierro, Head of World Market Intelligence at BASF Coatings
Why It Labored
Multi-agent structure with Genie as an agent presents a number of vital benefits for enterprises like BASF that look to leverage AI successfully of their enterprise contexts. We conclude the important thing power into the next points:
Specialised agent capabilities with excessive scalability and modularity: inside a multi-agent system, varied brokers can give attention to their particular domains or duties, enabling deeper experience in dealing with various queries and datasets. Furthermore, organisations like BASF can develop their gateway to AI options with an structure that permits every enterprise division to function independently whereas being centrally orchestrated. This modular design helps handle complexity over time.
Enhanced collaboration and improved consumer expertise: brokers can share data and context with each other, permitting for extra complete responses that combine knowledge from a number of sources. This facilitates smarter, sooner decision-making throughout varied enterprise features. By integrating AI endpoints to MSFT Groups as a chat interface, we permit customers to work together with brokers utilizing pure language, making it extra accessible to non-technical stakeholders.
Governance and compliance: Defending private and buyer knowledge is the Commented basis of Marketmind and stays our highest precedence. Each interplay is constructed on strict compliance with BASF’s knowledge safety requirements, leveraging Databricks’ enterprise-grade governance capabilities corresponding to Unity Catalog for fine-grained entry management, lineage monitoring, and auditability. This ensures that whereas Marketmind accelerates insights and actions, it does so inside a safe, clear, and absolutely ruled setting.
Shut crew work between BASF, Databricks and companions: From venture begin, BASF Coatings, Databricks account and product groups, and companion Accenture proactively engaged in workshops,. which helped align enterprise targets, technical necessities, and product imaginative and prescient, setting a robust basis for profitable implementation. Proper on time, hands-on classes created speedy suggestions loops. Knowledgeable steerage was repeatedly supplied by Databricks product crew, serving to to customise the answer for the complicated, evolving wants of BASF and making certain enterprise-grade high quality.
Trying Ahead: Multi-Layered Orchestration and Agent Bricks
With the success of the Marketmind multi-agent supervisor answer, the corporate is now increasing the enterprise impression throughout broader operations, together with Provide Chain, Procurement, Chemetall (Floor Expertise subsidiary), and Folks & Tradition. Along with our product crew, we’re exploring a extra scalable multi-layered structure, the place every division operates its personal multi-agent supervisor, whereas a higher-level Coatings-wide orchestrator serves all customers. This hierarchical system – a “supervisor of supervisors” – strikes the fitting stability: it allows division-scoped knowledge and power entry management, preserves flexibility in agent improvement, and helps a Coatings-wide “Ask Me Something” functionality.
Certainly one of our future enhancement objectives is the adoption of Agent Bricks, launched this 12 months on the Knowledge & AI Summit. Whereas our present Mosaic AI–based mostly answer helps multi-agent orchestration, it stays code-first and requires a extra hands-on strategy with added complexity in deployment and administration. Agent Bricks presents a streamlined approach to construct and optimize domain-specific, high-quality AI agent programs for frequent use circumstances, together with multi-agent setups. With options corresponding to automated optimization, price and high quality effectivity, and user-driven suggestions mechanisms, it simplifies agent implementation and permits groups to give attention to core challenges – knowledge, metrics, and problem-solving. Though we’ve not but been in a position to absolutely check its capabilities resulting from restricted regional availability, we view Agent Bricks as a visionary course and plan to allow integration as soon as it turns into accessible, accelerating division-specific multi-agent supervisor improvement.
