AI agents are spreading rapidly across enterprises, but most lack the business context needed to make reliable decisions. As organizations deploy thousands of autonomous agents across analytics, sales, support, and finance teams, a critical gap has emerged: agents can execute tasks, but they often do so without understanding the organizational rules, data definitions, and business logic that should guide their actions. Two major developments this week highlight how companies are addressing this fundamental problem.

Why Business Context Matters More Than Raw Intelligence?

The shift in how enterprises evaluate agent performance reveals a crucial insight. "Just 'doing' is no longer the standard by which we judge any agentic function's worth, suitability or credibility," according to analysis of current enterprise practices. Data science teams are now asking whether agents understand the organization's specific rules, definitions, and priorities, not just whether they can complete a task.

WisdomAI, a data intelligence platform, announced this month that it is releasing WisdomAI Analytics Agents, software designed to allow data engineers to design, test, and deploy AI-powered agents that reason and act upon data stacks autonomously while maintaining organizational context. The company's approach centers on what it calls the Adaptive Context Engine, a system that captures and preserves business logic across workflows.

"We continue to invest in data and BI capabilities that help surface insights faster and make them more accessible and actionable across the organisation. WisdomAI Agents enable teams to explore data interactively and uncover business drivers. It's helped us deliver tailored daily intelligence to our client-facing teams, enabling them to engage clients proactively with timely, relevant insights in fast-moving, dynamic markets," said Michael Caruana.

Michael Caruana, Tech Lead for Data Engineering and BI at Trumid

The Adaptive Context Engine works by extracting metric definitions, calculation rules, entity relationships, and naming conventions from existing documentation like data dictionaries, SQL queries, and team wikis. This creates what WisdomAI calls a "living ontology" that compiles organizational knowledge into machine-readable rules that every agent inherits at runtime.

How Are Companies Governing Thousands of Agents at Scale?

The governance challenge is becoming urgent. A year ago, many organizations had a dozen AI agents. Today, some have thousands, with every developer, analytics team, and business unit deploying autonomous systems. But without centralized governance, organizations face a critical question: which agents are accessing sensitive customer data, and how can you audit what they actually did?

Databricks announced an extension to its Unity Catalog that will expand governance infrastructure to cover every asset an AI system touches, including language models, MCP (Model Context Protocol) servers, skills, and agents themselves. The approach treats agent governance as fundamentally a data governance problem, not a separate AI-specific challenge.

The governance framework operates across three layers:

• Identity and Permissions: Agents inherit the invoking user's data permissions in real time through token passing, not shared service accounts. Every action is logged against both the user who triggered the request and the agent that acted on their behalf.
• Service Policies: Specific tool calls are evaluated before execution based on the tool name, arguments, and caller identity. Policies can allow, deny, or request user consent for each action.
• Guardrails: Runtime inspection scans inputs for personally identifiable information and jailbreak attempts, checks outputs for hallucinations and sensitive content before they reach users.

What's the Technical Difference Between Context-Aware and Generic Agents?

The fundamental difference lies in how data flows through agent workflows. Most agent frameworks, including LangChain and CrewAI, pass unstructured text between workflow steps. By the third or fourth step in a workflow, the agent is reasoning over an approximation of the data, not the actual data itself.

WisdomAI takes a different approach by preserving dataframe structures, column names, data types, relationships, and metadata at every step of the workflow. This ensures that agents work with actual data, not interpretations of it. The company also implements self-correcting workflows that automatically detect and fix logic errors, schema mismatches, and data quality issues without manual intervention.

"Agent frameworks like LangChain and CrewAI default to passing unstructured text between steps. There's no native dataframe contract, no schema validation at each node, and no guarantee that column names, types, or relationships survive the handoff. By the time you're three steps into a workflow, the agent is reasoning over an approximation of your data, not the data itself," explained Soham Mazumdar.

Soham Mazumdar, Co-founder and CEO of WisdomAI

How to Build Enterprise-Ready AI Agents in Minutes

Both platforms are addressing the speed-to-deployment challenge that has slowed enterprise adoption. WisdomAI's Agent Builder allows users to describe what they need in plain English, and the system assembles the workflow automatically, including nodes, logic, and connections. Users can then fine-tune the workflow using a drag-and-drop canvas and deploy enterprise-ready agents in minutes rather than weeks.

• Natural Language Workflow Design: Users describe requirements in plain English rather than writing code, reducing the technical barrier to agent creation and enabling non-engineers to build agents.
• Pre-built Connectors: WisdomAI offers 200 plus native integrations and MCP connectors that eliminate expensive ETL pipelines and data migration costs, allowing agents to query data where it lives.
• Full Observability and Auditability: Every step of an agentic workflow is fully auditable, allowing teams to replay exactly what happened, inspect each decision, and understand how a result was produced.
• Deterministic Outputs: Agents deliver the same result every time they run, meaning business teams can trust that reports generated on Monday will match those generated on Friday with no surprises.

The observability requirement has become non-negotiable as AI regulations emerge. Databricks' Unity AI Gateway writes the full payload of every model call to inference tables, capturing the exact prompt sent, the exact response returned, token counts, and latency. These audit logs land in the lakehouse as queryable tables, allowing organizations to analyze agent behavior using standard SQL rather than specialized logging tools.

Why Are Enterprises Moving Away From Centralized Data Warehouses?

A significant shift is happening in how agents access data. Traditional analytics required extracting, transforming, and loading data into a centralized warehouse before any system could reason over it. MCP connectors flip this model by giving agents direct, governed access to source systems at query time, whether that's Snowflake, Databricks, Salesforce, or SharePoint.

For unstructured sources like PDFs, contracts, and invoices, WisdomAI materializes a structured table on the fly that is queryable and joinable against the warehouse without a separate ingestion job. Access governance is enforced at the MCP connector level through the Adaptive Context Engine, applying row-level security, column-level security, and role-based access control at query time rather than baking it into a pipeline.

This approach eliminates the traditional ETL bottleneck. Organizations add a new data source by registering a connector, not by building and maintaining an ETL job. The result is faster deployment, lower infrastructure costs, and more current data flowing through agent workflows.

The convergence of context-aware agent design and centralized governance represents a maturation of the agentic AI space. Early agent deployments prioritized speed and capability; enterprises are now demanding reliability, auditability, and alignment with organizational rules. The platforms emerging this week suggest that the next phase of AI agent adoption will be defined not by raw intelligence, but by how well agents understand and operate within the specific context of the organizations deploying them.