Mosaic-First Agent Architecture on Databricks: From Vector Search to Governed Autonomy

Enterprises are graduating from one-off chat to agents that observe, reason, act, and learn. A Mosaic-first approach makes that leap practical: start with governed retrieval, layer in reasoning, then orchestrate actions so outcomes are reproducible, auditable, and safe. 

Chatbots talk; enterprise agents do. Built on Databricks Mosaic AI and the Lakehouse, ACI Infotech’s agents read your policies and dashboards with entitlement‑aware retrieval (Mosaic AI Vector Search + Unity Catalog), reason with DBRX, and take auditable actions through Databricks Workflows closing the loop from question to outcome.  

Reconciliations run themselves, support triage pre‑fills and routes, and compliance checks self‑document with citations. Our clients have realized up to 3× faster regulatory audits, 20% lower forecasting costs, and 60% greater operational transparency. As resilience and agility become non‑negotiable, agentic architectures deliver not just answers but foresight anticipating risks, recommending next steps, and improving with every interaction. 

Mosaic AI 101: Capabilities that Power Agents 

Generative AI gave us conversational prowess; Agentic AI adds operational autonomy. On Databricks, Mosaic AI grounds autonomy by pairing models with Unity-governed data, retrieval, and tooling for real outcomes. 

Four Mosaic-ready capabilities: 

• Observation: Ingest SQL and files, embed via Mosaic AI Vector Search, enforce Unity Catalog permissions. 
• Reasoning: Use DBRX with context to plan multi-step solutions. 
• Action: Orchestrate tools and APIs via Databricks Workflows, with prompts and MLflow runs. 
• Learning: Persist memories in Delta and vectors, close loops with evaluations, cost metrics, guardrails. 

In short, Generative AI generates. Agentic AI powered by Mosaic acts with memory, governance, and purpose. 

Architecting Agentic AI on Databricks 

Building autonomous enterprise systems requires a carefully designed foundation. Databricks unifies every layer data, models, orchestration, and governance into a cohesive framework that accelerates the development and reliable deployment of AI agents. 

1. Semantic Intelligence with Mosaic AI Vector Search

At the heart of every intelligent agent lies semantic retrieval, enabling systems to access knowledge efficiently and contextually. Mosaic AI Vector Search transforms massive datasets documents, records, communications into vector embeddings managed through Unity Catalog. 
This allows AI agents to: 

• Retrieve semantically relevant content instantly. 
• Maintain enterprise-level access control and governance. 
• Integrate retrieved context directly into dynamic workflows and reasoning chains. 

This transforms unstructured enterprise knowledge into searchable, actionable insights. 

2. Foundation Model APIs: Cognitive Core of the Agent

Hosted Databricks Foundation Models like DBRX and LLaMA 2 form the cognitive layer of these agents. By coupling these models with secure, in-context data from Vector Search, enterprises can create agents capable of understanding complex intents, planning next steps, and executing tasks autonomously. 
Typical applications include: 

• Automated helpdesk assistants that troubleshoot through reasoning loops. 
• Financial analysis bots that parse performance data and suggest optimization strategies. 
• Compliance advisors that interpret and rewrite policies across multiple jurisdictions. 

3. Workflow Orchestration with Databricks Workflows

Intelligent agents aren’t standalone models they’re orchestrated systems. Databricks Workflows provides a robust mechanism to control tool executions, trigger pipelines, or connect to enterprise APIs. 
Through task orchestration, agents can sequentially reason, validate results, and execute decisions. For instance: 

• An AI sales assistant can analyze CRM data, identify prospects, generate follow-ups, and schedule outreach all autonomously. 
• A manufacturing agent can read real-time IoT data, adjust thresholds, and generate maintenance tickets dynamically. 

4. Memory Systems and Feedback Loops

Long-term memory transforms AI from reactive to relational. Using Delta Lake tables or vectorized memories, agents can store historical interactions, user preferences, and results. This persistent knowledge base ensures that every new action benefits from prior experience creating continuous improvement loops within the enterprise ecosystem. 

5. Governance, Monitoring, and Compliance

With great autonomy comes the need for accountability. Databricks and ACI Infotech emphasize responsible AI through: 

• Prompt traceability with MLflow and Unity Catalog. 
• Guardrails for PII protection and regulatory compliance. 
• Cost transparency through detailed inference and token tracking dashboards. 

These mechanisms help ensure that enterprise AI remains explainable, cost-efficient, and ethically governed. 

Mosaic-Native Patterns: Real-World Agentic Workflows 

Organizations are already transforming operations with Databricks Mosaic AI where Vector Search, Unity Catalog governance, Model Serving, and Workflows power end-to-end agents: 

• Legal Research & Case Discovery: Mosaic AI Vector Search indexes briefs and precedents under Unity Catalog permissions; DBRX summarizes and cites sources; MLflow logs prompts/runs for auditability shrinking research cycles by orders of magnitude. 
• Autonomous Analytics Bots: Users ask, “What drove margin decline last quarter?” Agents retrieve facts via Vector Search, reason with DBRX, and trigger Databricks Workflows to compute fresh metrics; outputs and assumptions are tracked in Delta + MLflow for reproducibility. 
• Sales Enablement Agents: Product specs, CRM notes, and call transcripts live in governed Vector Search collections; DBRX crafts tailored pitches; Workflows hit CRM/Calendar APIs to draft follow-ups and schedule next steps while PII guardrails enforce access controls. 
• Finance & Invoice Copilots: Line-items and GL entries are embedded into Vector Search; agents reconcile deltas with Spark jobs orchestrated by Workflows, flag anomalies, and generate DBRX-written auditor explanations cost and token usage monitored in dashboards. 

Together, these Mosaic-native patterns turn enterprise knowledge into secure, self-improving workflows from strategy to operations with governance and observability built in. 

Why ACI Infotech: Exclusive Partnership & Proven Delivery 

As an exclusive strategic partner of Databricks, ACI Infotech is uniquely positioned to architect and operationalize agentic AI across enterprise landscapes.  

What Sets Us Apart 

• Exclusive Databricks Partnership: ACI Infotech brings deep access to advanced Databricks capabilities, including Mosaic AI, Lakehouse, Agent Bricks, and Foundation Model APIs. This allows our clients to tap into next-gen features the moment they’re launched turning platform innovation into real operational advantage.  
• Tailored Enterprise Solutions: We design agentic AI architectures that fit your specific industry, regulatory, and business model needs. From rapid prototypes to full-scale production deployments, our frameworks bridge the gap between vision and value. 

Delivery Highlights: Projects That Set the Benchmark 

• Healthcare: Built a clinical knowledge assistant for major providers, instantly answering complex queries from over 10 million documents drastically accelerating insights and care quality.  
• Retail: Migrated a global brand to Databricks’ unified Lakehouse architecture, enabling real-time demand forecasting and reducing data platform costs by 40%.  
• Financial Services: Automated compliance agents streamlined policy reviews, cut manual workloads, and delivered 2x faster decision-making at major banks.  
• Manufacturing: Deployed predictive maintenance and supply chain AI, boosting production uptime and lowering overall costs by 20%.  

Every project is engineered for rapid business impact clients see faster value realization, sharper insights, and lower risks, all backed by secure, governed deployment practices unique to ACI’s enterprise approach.