I build Agentforce agents and I build custom AI agents. Different clients, different problems, different tools. The question I get most is some version of: "which one should we use?"
The answer comes down to three things: where your data lives, where your users work, and how much control you need. Let me walk through how I think about it.
Quick Definitions
Agentforce is Salesforce-native. Agents run inside the Salesforce ecosystem, access your org's data without custom integrations, and can be embedded in Service Console, Experience Cloud, or Slack. They inherit your sharing model and field-level security automatically.
Custom agents (what I build under the OpenClaw offering) run on LLMs like Claude or GPT, connect to your systems via API, and deploy to Slack, WhatsApp, or Telegram. They're not tied to any one platform. I define the skills, data connections, persona, and deployment target from scratch.
Both use large language models under the hood. Both can execute tasks and interact conversationally. The difference is in the architecture and the trade-offs.
Agentforce Wins Here
Agentforce is the shorter path when the use case is Salesforce-centric. If the data lives in your org and the people using the agent work inside Salesforce, don't overcomplicate it.
Service deflection is Agentforce's home turf. An agent handling customer inquiries using Knowledge articles, case data, and account info. It lives in your Experience Cloud portal, creates cases, updates records, escalates to humans. All within the Salesforce security model. I've stood these up and they work.
Sales process support is getting better fast, especially with the Spring '26 investment ("Agentforce Sales"). Agents that qualify leads, summarize account activity, or prep meeting briefs using CRM data. The data is already structured and accessible.
Compliance-sensitive workflows are a big one. Agentforce inherits your org's sharing model, profiles, permission sets. If you need the agent to respect the same data access rules as your users, that's built in. With a custom agent, you're building that authorization layer from scratch. I cover this more in my RAG architecture post, but it's meaningful engineering effort.
The pattern: if the question is "how do I make Salesforce smarter for people already inside Salesforce," Agentforce is the answer.
Custom Agents Win Here
Custom agents make more sense when the use case extends beyond Salesforce, when the users don't live in Salesforce, or when you need the agent to pull from multiple systems in a single interaction.
Slack-first teams. I have clients where the field team opens Salesforce maybe once a week. Their world is Slack. Putting an agent inside Salesforce doesn't help those people. A custom agent in Slack that queries Salesforce, their ERP, and a data warehouse gives them what they need without changing how they work.
Multi-system orchestration. Check inventory in the ERP, pull client data from Salesforce, look up shipping status from a third-party API, summarize it all in one response. Agentforce can call external APIs, but custom agents are built from the ground up for this kind of cross-system work.
Non-Salesforce data as the primary source. If most of what the agent needs lives in a data warehouse, a product database, or internal docs outside Salesforce, a custom agent with direct connections to those systems is more efficient than routing everything through Salesforce as a middleman.
Speed. Custom agents go from concept to production in weeks with no dependency on Salesforce release cycles, org configuration, or license availability. If you want to test an AI agent concept fast, this is the faster path.
Both. Sometimes the Answer is Both.
Not a cop-out. I've seen this work well.
Agentforce handles the Salesforce-native interactions: service deflection, internal record queries, sales process support. A custom agent in Slack handles the cross-functional stuff that doesn't fit in the Salesforce UI. Different users, different contexts, minimal overlap.
Where it gets interesting: the custom Slack agent can write data back to Salesforce. A field rep asks the Slack agent a question, the agent pulls from multiple systems, and logs the interaction as a Task for audit purposes. The two agents aren't competing. They're serving different layers of the same ecosystem.
The Quick Decision Framework
Agentforce if:
- Primary data source is Salesforce
- Users work inside Salesforce or Experience Cloud
- Sharing and security model matters
- You're invested in the Salesforce AI stack (Data Cloud, Einstein)
- Use case is service, sales, or CRM-centric internal ops
Custom agent if:
- Primary workspace is Slack, WhatsApp, or Telegram
- Agent needs data from multiple systems
- You need full control over the LLM, prompts, and behavior
- Speed to production matters more than platform alignment
- Use case doesn't require Salesforce record access at all
Both if:
- Salesforce-heavy internal users AND Slack-heavy cross-functional teams
- Different user groups have fundamentally different tool preferences
- You need native CRM automation AND multi-system intelligence
One More Thing
The Salesforce ecosystem would like you to believe Agentforce is the answer to every AI question. It's not. It's a strong platform for CRM-centric use cases and it's getting better fast (Spring '26's Builder and Agent Script are real improvements). But most organizations I work with have workflows, data, and people that extend well beyond Salesforce.
The best implementations I've delivered are the ones where someone took the time to map use cases to the right tool instead of defaulting to whatever the vendor rep pitched.
Not sure where your use case lands? Grab a call and I'll give you my honest take.