Agentforce & AI on Salesforce

AI that's connected to the process.
Not a demo disconnected from the work.

Bringing AI into an organization starts with a defined process, reliable data, clear permissions and human oversight. We help identify the right use cases and integrate them into your Salesforce environment in a phased, controlled way.

Explore Agentforce fit Check an existing system

Who this is for

Typical starting points for AI on Salesforce

Organizations already running Salesforce who want to identify where AI truly adds value.
Service teams with high case volumes looking to cut handle time without hurting quality.
Sales teams that want to save time on prep, summaries and follow-up.
Managers who struggle to extract insight from existing organizational data.
Organizations that require governance and control before putting AI into production.

Possible use cases

Where AI genuinely adds value

Case and call summarization
Enterprise knowledge search
Next Best Action recommendations
Self-service
Case classification and routing
Task creation
Filling missing information
Drafting responses
Prepping reps before a call
Anomaly detection
Manager reporting assistance
Agentforce for defined processes

Problems this service solves

Why AI projects get stuck at POC

Enthusiasm without focus

Many organizations start from the technology instead of the problem. We identify two to three use cases that occur frequently, are measurable and improvable, and focus there first.

Data that isn't ready for an agent

AI exposes every flaw in your data — empty fields, duplicates, conflicting sources. We check the data before the agent is built.

Regulatory and security concerns

What the agent sees, what it's allowed to do, how it's logged. We define a Trust Layer, prompt policy and audit trail before go-live.

POC projects that never reach production

A POC with no defined success criteria stays a nice demo. We define upfront what counts as 'passed' and how it's measured.

Working principles

A responsible approach to adopting AI

Start from the process

We don't add AI just because the capability exists. We define the problem, the user, and the desired outcome first.

Check the data

An AI agent cannot compensate for unreliable data, wrong permissions, or an unclear process.

Keep humans in control

Actions with real business, financial or service impact need an appropriate approval mechanism.

Define permissions

The agent only gets access to the data and actions it actually needs for its role.

Start with a narrow scope

Build one use case, measure the outcome, and only then expand.

Measure quality

Track accuracy, usage, time saved, exceptions and user feedback.

Implementation process

Eight controlled stages

01
Select a use case
02
Check data and permissions
03
Design the conversation and actions
04
Build a prototype
05
Test with users
06
Define guardrails
07
Phased rollout
08
Monitoring and refinement

Decision point

A short conversation about your first use case

We'll flag together which processes are ready for AI, and which are better fixed at the process level before bringing in an agent.

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Decision factors

Four decisions that determine whether AI reaches production

Built-in Agentforce vs. custom development

When to choose built-in Agentforce Actions, and when dedicated tool development or a call to an external system is required.

Scope of agent autonomy

Deciding how much the agent acts independently, when it proposes for human approval, and when it hands a task off entirely to a rep.

Pricing and operating model

AI usage consumes credits. Correctly sizing context, prompt length and usage frequency directly affects operating cost.

Monitoring and continuous improvement

Measure not only the agent's output but also user behavior around it — which actions were approved, which were rejected, and what gets escalated to a human.

Common mistakes

Patterns worth avoiding in AI implementation

Building an AI agent before there's a defined business process.
Granting the agent overly broad permissions for development convenience.
Exposing an unvetted data source and trusting the model to 'figure it out.'
Skipping human approval for actions with financial or service impact.
Launching without logs and monitoring — leaving no way to learn and improve.

Go deeper

Related guides and services

FAQ

Agentforce & AI — questions we hear often

What is Agentforce?

Agentforce is Salesforce's AI layer for building autonomous agents that carry out tasks within sales, service and operational processes — with governed access to data, permissions, external tools, and human approval mechanisms where needed.

Do we need Data Cloud to use Agentforce?

Not necessarily. Some use cases work directly against standard Salesforce data. Data Cloud becomes relevant when the agent needs to draw on multiple sources, unstructured data, or real-time information from external systems.

What's the difference between Agentforce and a chatbot?

A traditional chatbot follows a predefined script. Agentforce uses a language model to understand intent, choose tools, act on data and hold a conversation. It also knows how to hand off a task to a human when the scenario falls outside what it's been given.

Does using AI expose sensitive information?

Not when implemented correctly. The agent operates under Salesforce's permission layer, you can define a Trust Layer, block sensitive fields, and log every action. We define governance and controls before an agent goes into production.

How do we know a use case is right for AI?

AI adoption makes sense when there's meaningful volume of the same repeated task, when the relevant information exists and is well organized, when there's a measurable outcome, and when there's a clear cost to continuing manual execution. Use cases lacking these traits usually won't justify the investment.

Next step

Explore Agentforce fit

We'll assess which scenarios are ready for an AI agent, what's needed in data and permissions, and where to start.