A practical guide to Agentforce deep research

Let’s be real, the thought of an AI agent that handles all your deep research, prepping for meetings, digging into market trends, or pulling up customer histories, sounds like a dream come true. Salesforce is stepping into this space with its Agentforce platform, promising an AI that can do just that.

But what does "Agentforce deep research" really mean in practice?

In this guide, we’ll pull back the curtain. We’ll break down what this feature is, what it really takes to get it working, its practical limits, and the actual costs involved. We’ll explore why it often gets stuck inside the Salesforce ecosystem and how other tools can give you a much bigger picture by connecting to all your company’s knowledge, no matter where it’s stored.

What is Agentforce deep research?

On paper, the Agentforce deep research agent is supposed to be your behind-the-scenes assistant. It’s designed to process data and handle those long, tedious tasks for any team, from sales to support. According to Salesforce, it can tackle market analysis, gather data for strategy meetings, and deliver insights to make everyone more productive.

It runs on something called Salesforce’s Atlas Reasoning Engine. Fancy name, but all it really means is that it tries to understand your question, figures out the steps to get an answer, and then goes to work. For deep research, this means it mainly digs through data that’s already inside the Salesforce ecosystem, like your CRM data, customer support history in Service Cloud, and anything else you’ve managed to pull into the Salesforce Data Cloud.

An example of an Agentforce AI agent handling a data request within Slack.

The whole point is to save people from hours of manual work. For instance, a sales manager could ask it to, "summarize all high-value opportunities in the pipeline and flag key risks based on recent support tickets." The agent would then dive into Sales Cloud and Service Cloud to spit out a report. It sounds great, but it all hinges on one massive assumption: that your data is perfectly organized and living entirely within Salesforce.

Setting up for Agentforce deep research: Requirements and reality

Getting Agentforce deep research up and running isn’t as simple as flipping a switch. It’s a full-blown enterprise project, and honestly, the list of requirements can be a major hurdle for a lot of companies.

The central role of Salesforce Data Cloud

For Agentforce to do any useful research, it needs good data. And for Salesforce, that means your company has to first commit to implementing and corralling all your information inside the Salesforce Data Cloud. This isn’t a small task. It involves a whole lot of work from your data team to pull in, clean up, and organize data from all over the place. If your best insights are buried in unstructured documents outside of Salesforce, they’re basically invisible to the agent unless you take on this huge data centralization project first.

The external data challenge

Let’s face it, your company’s brain is scattered everywhere. Product specs are in Confluence, big decisions happen in Slack, and the real strategy docs are probably sitting in Google Docs. Agentforce has a tough time peeking into those places on its own. To connect to these outside sources, you need to set up complex, custom integrations (often using tools like MuleSoft), which adds a hefty price tag, a long development timeline, and more maintenance work down the road.

This creates a massive blind spot. An agent doing "deep research" without seeing your main knowledge bases can only give you a narrow, CRM-focused sliver of the story.

This is where a different way of thinking really pays off. Instead of forcing you into a giant data migration project, a tool like eesel AI is built to connect to the tools you already use in a flash. With one-click integrations for platforms like Confluence, Google Docs, and Slack, it pieces together your company’s knowledge in minutes, not months, and works the way your team already does.

Agentforce deep research in action: Use cases and key limitations

Once you finally get it configured, the Agentforce deep research agent can handle some specific tasks. But its limitations show up pretty quickly when you throw real-world business problems at it.

Potential use cases within Salesforce

So, where does it actually shine? Mostly, when you stay inside Salesforce’s four walls. For example, it can handle requests like:

• Analyzing sales performance: "Show me the win rates for deals that included a product demo last quarter."

• Summarizing customer history: "Give me a summary of Acme Corp’s last five support cases and their outcomes before my call."

• Identifying at-risk customers: "List all enterprise customers with dropping product usage and no support tickets in the last two months."

The Agentforce Service Agent is shown handling a customer query within the Salesforce dashboard.

The walled garden limitation

Here’s the biggest catch with Agentforce deep research: it lives in a "walled garden." And you don’t have to take our word for it, just look at the frustrated Reddit threads out there. People are finding that the AI just can’t find what they need because, surprise, the answers aren’t sitting neatly in the CRM.

Real deep research needs a complete view of company knowledge, which usually includes:

• Technical docs in a wiki.

• Internal debates about product features.

• Marketing campaign plans.

• Customer feedback from other platforms.

An agent that can’t see any of that is working with one hand tied behind its back. It often serves up incomplete or even irrelevant answers, which means your team ends up doing the manual research anyway, defeating the whole purpose. In contrast, eesel AI’s AI Internal Chat was built for this messy, multi-platform reality, giving your team a single AI assistant that learns from your entire knowledge ecosystem.

The true cost of Agentforce deep research

Okay, let’s talk money. Because beyond what Agentforce can and can’t do, the total cost is a real eye-opener.

The direct pricing model

Salesforce has shared its pricing for Agentforce, which starts at $2 per conversation or lead. A pay-as-you-go model like this means your bill can swing wildly from one month to the next. If your support or sales teams have a busy month, you could be looking at a much higher bill than you expected, making it really tough to budget. You’re essentially penalized for getting your team to use the tool more.

Hidden implementation and maintenance costs

But that initial price tag? That’s just the start of it. The real cost to get Agentforce doing effective deep research includes:

• Salesforce Data Cloud Licensing: You have to pay for the data platform that Agentforce relies on, and that comes with its own big licensing fees.

• Integration Development: You’ll need to budget for Mulesoft licenses and developers to connect any non-Salesforce tools that hold important information.

• Consulting and Services: As you can see from partners like Slalom and TechForce, many companies end up hiring expensive consultants to handle the tricky setup and implementation.

• Internal Resources: Your own admins and developers will have to sink a lot of time into setting up, testing, and keeping the agents running smoothly.

A much simpler way to go is a flat subscription fee. eesel AI’s pricing is all about being clear and predictable. With straightforward monthly or annual plans that include unlimited bots and all integrations, you get powerful AI without worrying about costs spiraling out of control. There are no per-resolution fees, and you can get started yourself without needing a team of consultants.

Cost Factor	Salesforce Agentforce	eesel AI
Pricing Model	Usage-based ($2/conversation)	Flat subscription (starts at $239/mo annually)
Predictability	Low (costs scale with usage)	High (fixed monthly/annual fee)
Integration Costs	High (requires Mulesoft/custom dev)	Included (100+ one-click integrations)
Setup Cost	High (often requires consultants)	Low (designed for self-serve setup)
Hidden Fees	Yes (Data Cloud, custom dev, etc.)	No (transparent, all-inclusive plans)

Agentforce deep research: A powerful vision with practical hurdles

So, what’s the final word on Agentforce? The vision is definitely impressive. But when you get down to it, there are some serious practical roadblocks. The biggest ones are being locked into the Salesforce world, the headache of setting it up, and a pricing model that can feel like a guessing game. For most teams, the "deep research" it offers feels a bit shallow because it can’t see all the important stuff happening outside the CRM.

Before you go all-in on a closed system, it’s worth asking what your team actually needs: an AI assistant that works where they do. The best research tools are flexible, simple to set up, and can tap into your company’s collective brain across all the apps you use every day.

Unlock your team’s true knowledge with eesel AI

If you’re looking for an AI assistant that can securely and instantly answer questions using knowledge from your entire tech stack, from Confluence and Google Docs to Slack and Zendesk, then Agentforce’s walled garden probably isn’t the right fit.

With eesel AI, you can get a powerful AI agent up and running in minutes, not months. You can give your team the complete answers they need, without all the complexity and hidden costs.

Start your free trial today