What is Enterprise Deep Research (EDR) and Why It Matters

Enterprise Deep Research (EDR) is transforming how organizations approach complex research tasks by leveraging advanced AI and multi-agent systems. Present enterprise deep research is a multi-agent solution designed for autonomous, reasoning-intensive research within enterprise environments, enabling adaptive, transparent, and user-guided knowledge synthesis.

EDR’s architecture is built as a steerable multi agent deep system, allowing for flexible, human-in-the-loop interaction and real-time steering. This design supports modularity, adaptability, and high performance in processing unstructured data and generating actionable insights for domain-specific research scenarios.

Getting Ready for the Next Leap in Enterprise Intelligence

Salesforce Research just dropped a massive game-changer: Enterprise Deep Research (EDR), a steerable, multi-agent system that turns the chaos of enterprise data into actionable insights you can actually use. In enterprise settings, organizations face unique operational challenges such as dispersed information sources, rapidly shifting domain knowledge, and the need for transparent, adaptable reasoning tailored to their context.

As unstructured enterprise data piles up, organizations need more than just pretty dashboards - we need context-aware reasoning systems that can understand what’s going on, connect the dots, and get you the answers you need, fast.

EDR isn’t just another tool. It’s an architecture designed for continuous learning, contextual understanding, and deep domain adaptability - the same stuff that made Agentforce 3 the foundation of the agentic enterprise.

EDR: Finally, Some Sanity in the Data Chaos

Unveiled through Salesforce AI Research, EDR brings a brand new agentic structure built from the ground up for enterprise-scale research and decision making.

The MCP-Based Integration Layer serves as the backbone for connecting EDR with various enterprise systems. This layer enables seamless enterprise integration, allowing unstructured enterprise data to be connected and utilized efficiently within multi-agent systems.

In addition to the core integration, EDR supports additional domain tools such as code search and database querying, which enhance the capabilities of enterprise connectors and improve interoperability across different business environments.

The Core Components

Master Planning AgentThis is the brains of the operation—breaking down complex enterprise questions into smaller, more manageable chunks that any research assistant or specialized agent can handle. The system continuously evaluates agent efficacy, using quality control mechanisms and adaptive task management to ensure optimal performance and output quality.

Four Super-Smart Specialized Search AgentsEach research assistant is a specialized agent with a domain-specific role:

• General Agent: Pulls insights from all over the web, internal reports, databases, and document repositories.

• Academic Agent: Digs up scholarly and scientific literature, leveraging document repositories and academic databases.

• GitHub Agent: Mines tech data from repositories and codebases, acting as a specialized agent for technical research.

• LinkedIn Agent: Connects the dots between human capital, skills, and org context, with strict domain restriction to linkedin.com for precise enterprise-focused queries.

These specialized agents translate natural language queries into structured tasks, considering schema context and validation processes. Tasks are articulated using natural language descriptions, making them human-readable and easy to manage.

MCP-Based Integration LayerThis is where the magic happens—the same interoperability framework used by Agentforce 3, which allows seamless integration across any system, data warehouse, or workflow. Prompts embed temporal markers such as CURRENT_DATE and CURRENT_YEAR to ensure responses are relevant and recency-sensitive, especially when incorporating user-uploaded documents and external evidence.

Visualization AgentTransforms findings into dynamic, data-driven visuals that make sense to execs.

Reflection MechanismA built-in reasoning layer that detects knowledge gaps, refines results, and keeps going until it gets it right. The reflection phase is a specific point in the workflow where the system processes queued user messages and directives atomically, maintaining stability and alignment. The full reasoning process is captured, including search, reflection, and synthesis steps, providing a detailed understanding of decision-making and planning behaviors.

The system determines when to stop iterating based on sufficient report completeness, ensuring all knowledge gaps are adequately covered before concluding the research.

The EDR Research Flow: How Deep Research Actually Happens

So, what’s really going on under the hood when you fire off a research request in EDR? This is where the magic of enterprise deep research comes alive—a multi-stage, orchestrated process that turns your wildest, messiest data into laser-focused, actionable insights.

It all kicks off with the master planning agent. Think of this as your research conductor, taking your high-level research objectives or natural language queries and breaking them down through adaptive query decomposition. Instead of dumping everything on one agent, EDR spins up a team of specialized search agents—each one laser-focused on a different domain, from academic search to LinkedIn search, internal documents, or code repositories. These agents don’t just fetch data; they analyze, compare, and synthesize, ensuring every angle is covered.

As the research unfolds, the research todo manager keeps everything on track, dynamically updating tasks and surfacing new knowledge gap tasks as they’re detected. The reflection mechanism is always running in the background, scanning for missing pieces, updating the research direction, and making sure the process never stalls. If a knowledge gap pops up, EDR’s agents pivot, dig deeper, and keep iterating until the gaps are filled.

Handling heterogeneous enterprise data—from unstructured files to real-time streaming sources—is where EDR really flexes. The file analysis component preserves document layout and context, while metadata extraction and semantic content analysis ensure nothing gets lost in translation. Selective raw content retention means only the most relevant data makes it through, cutting down on noise and information overload.

Integration is seamless. EDR plugs directly into your enterprise workflows and custom enterprise systems via direct API integration, so research flows naturally alongside your existing processes. Context aware prompt engineering and the model context protocol ensure that every user query and natural language directive is translated into precise, actionable tasks—no more lost-in-translation moments.

But EDR isn’t just about automation. Human steering is built in at every stage.

You can issue natural language directives, provide user input, and steer the research flow as needed. The system’s intent alignment keeps everything focused on your organization’s goals, while cross agent result comparison and reflection phases guarantee that findings are robust, consistent, and fully auditable.

When it’s time to present results, the visualization agent transforms all that deep research into clear, data-driven insights—think structured reports, dynamic dashboards, and even example generation to illustrate key points.

Source citation management and semantic consistency validation ensure every claim is backed up and every insight is trustworthy.

And because every enterprise is unique, EDR’s multi agent deep research framework is fully customizable. Whether you’re dealing with strict domain restrictions, unique data characteristics, or specialized retrieval needs, EDR adapts—scaling up with exponential context growth and leveraging steerable context engineering to keep research relevant and on point.

Bottom line: the EDR research flow is a living, breathing system—combining autonomous agents, real-time data processing, and human guidance to deliver progressively refined knowledge representation and truly actionable insights. It’s not just deep research. It’s enterprise deep. And it’s about to change everything you thought you knew about enterprise analytics.

What Makes EDR So Damn Groundbreaking

EDR doesn’t just find information - it understands enterprise problems and gives you a structured, evidence-backed report that actually aligns with what matters to your business. Unlike prior benchmarks, EDR’s evaluation captures the entire reasoning process—including search, reflection, and synthesis steps—enabling a more comprehensive analysis of decision-making dynamics.

Prototypes have shown some crazy results:

• 70% faster turnaround on multi-source research reports, thanks to automated report generation

• 40% less time spent on manual data validation

• Consistent accuracy in aligning with org goals and compliance standards, validated and tested using internal datasets

For industries like finance, healthcare, and manufacturing, where decisions rely on multiple disconnected data systems, EDR represents a whole new level of operational intelligence.

Building on the Agentforce Legacy

If Agentforce 3 marked the beginning of the digital labor era, EDR marks the start of the digital reasoning era.

With MCP integration, existing Agentforce deployments can hook right in, so AI agents and research agents can start talking to each other. For example, a sales agent in Agentforce could automatically trigger an EDR query for market intelligence or competitor benchmarking, and get a full report without needing a human. The integration process is designed for seamless enterprise deployment, ensuring automated report generation and real-time streaming across enterprise environments.

Internal enterprise use cases and evaluations have demonstrated EDR’s high accuracy, reliability, and scalability within proprietary enterprise data environments, highlighting its benefits for enterprise analytics.

This tight integration between action (Agentforce) and analysis (EDR) is going to define Salesforce’s vision for 2026: a fully agentic enterprise ecosystem.

Trailblazers in Action

Though still in research phase, early adopters and partners are already experimenting with EDR frameworks:

• Think Tanks are using EDR to synthesize research across academic and economic sources

• Global Manufacturing Firms are testing EDR’s GitHub Agent to analyze internal repository activity and predict code maintenance risks

• Consulting Networks are leveraging the LinkedIn Agent to map talent clusters and optimize project team formation

Each case shows how multi-agent collaboration, combined with deep context reasoning, is going to transform enterprise learning and strategy. A research flow mechanism coordinates the actions of multiple agents and integrates human input, ensuring systematic, iterative progress and context preservation in enterprise analytics.

Preparing for the EDR Revolution

With Salesforce open-sourcing the EDR framework on GitHub and datasets available on Hugging Face, the innovation is going to come fast.

Trailblazers and partners should start looking into:

• MCP Integration for their own proprietary tools

• Custom Search Agent development for industry-specific knowledge graphs

• Visualization extensions to enhance data storytelling and analytics clarity

As with Agentforce, expect new certifications and guided Trailhead paths to follow, empowering devs and architects to build, train, and deploy their own research agents.

Looking Ahead: A Unified Agentic Intelligence

EDR isn't just a tool - it's a blueprint for how enterprise reasoning will work alongside enterprise action.

Together, Agentforce and EDR are laying the foundation for a new Salesforce era: where intelligent agents not only execute workflows but also analyze, contextualize, and advise.

In the next twelve months, this synergy is going to change the way organizations plan, decide, and innovate - turning data into direction, and AI into a partner that actually adds value.

Closing Takeaway

Enterprise Deep Research is the next big leap in AI orchestration.By bringing search, reasoning, and visualization together in a single, coordinated framework, Salesforce is once again pushing the boundaries of what we can achieve in the intelligent enterprise.

As Dreamforce 2025 gets underway - setting the stage for an actual transformation rather than just words about it - EDR makes one thing clear : the future of enterprise AI isn't just about machines being able to work on their own - it's about them being properly aware of their place in the world.