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AI agents that act inside the boundaries you set

Agentforce for service, sales and operations — agents grounded in your data, given the actions they need and the limits they must not cross, and handing off to people when they should.

Use-case discovery & readinessAgent design — topics & actionsPrompt Builder & knowledge groundingTrust Layer, testing & monitoring

An AI agent is a new member of staff with access to your systems. We treat it that way: a clear job, the right permissions, supervision while it learns, and a record of everything it does.

What we deliver

From first use case to agents in production

The four areas every Agentforce engagement covers — starting small, and scaling on evidence.

Use-case discovery & readiness

The requests and tasks worth automating first, ranked by volume and risk, and an honest check of whether the data, knowledge and permissions behind them are ready.

Agent design — topics & actions

Each agent given its topics, instructions and the actions it may take — flows, Apex, APIs and MuleSoft integrations — and nothing beyond them.

Prompt Builder & knowledge grounding

Prompt templates grounded in your records, knowledge articles and Data Cloud, so answers come from your facts rather than from the model's guesses.

Trust Layer, testing & monitoring

The Einstein Trust Layer configured for masking and audit, agents tested against realistic conversations before launch, and their performance monitored after it.

How we deliver it

From pilot to scale

  1. Pick one well-bounded use case

    High in volume, clear in its rules and low in the cost of a wrong answer — the right first job for an agent.

  2. Prepare data and knowledge

    Clean the records, fill the gaps in knowledge and set permissions, because an agent is only as good as what it can read.

  3. Build and test the agent

    Topics, actions and guardrails configured, then tested against a broad set of real-world prompts, edge cases included.

  4. Launch with a human in the loop

    Go live on one channel with escalation to people, and review conversations closely in the early weeks.

  5. Measure and scale

    Track resolution, escalation and satisfaction, then extend to the next use case on the evidence.

Use cases

Where agents earn their keep first

Good first agents take on work that is frequent, well documented and easy to hand to a person when it goes beyond them.

FAQs

Before you start

Is our data safe with Agentforce?

The Einstein Trust Layer masks sensitive data before it reaches a model, and Salesforce's agreements with its model providers include zero data retention. We configure those controls, and the agent's permissions, so it reaches only what it needs.

What happens when an agent cannot help?

It hands the conversation to a person — through Omni-Channel routing in Service Cloud, for example — with the history attached, so the customer never has to repeat themselves.

Where should we start?

With one use case that is high in volume and low in risk. Proving value there builds the confidence, and the data discipline, to take on the next one.

Case study

Where we have done this before

All case studies →

Grow faster than you ever imagined

Tell us which platform you are standing up, migrating off or trying to make behave. We will come back with the engineers, the model and a realistic sequence.

Talk to our expertsCall the team
+1-562-916-3390 · Los Angeles, CA