Agentforce and AI Automation
Practical Agentforce consulting: picking use cases that suit an agent, grounding it in your data, building safe actions and testing it before customers see it.
Most organisations looking at Agentforce have the same question: where would an AI agent actually help, and what has to be true in our org before it can? The technology is the easy part to demonstrate. The harder parts are choosing a job the agent can do well, giving it accurate data to work from, limiting what it is allowed to change, and proving it behaves before real customers or employees rely on it.
Who this is for
- Service leaders who want an agent to handle routine requests (order status, appointment changes, simple account updates) and hand everything else to people with context.
- Sales and operations teams who want an assistant inside Salesforce that summarises records, drafts follow-ups or prepares data for a task.
- Platform owners and architects asked to "turn on Agentforce" who want a plan that respects existing security and automation.
Where agents fit, and where they do not
An agent fits when the request is common, the answer depends on data you hold, the steps reduce to a few actions, and a wrong answer is recoverable. It fits poorly when the work needs unwritten judgement, the data is incomplete, or a mistake has regulatory or financial consequences a person should own.
Often the right answer for part of a use case is ordinary automation. If a process is fully deterministic, a record-triggered flow is cheaper to run and easier to test than an agent.
What we do
Use-case assessment
Score candidate use cases on volume, data availability, risk and measurability, and pick one to start with.
→Data and grounding readiness
Check that knowledge articles, records and, where used, Data Cloud (now branded Data 360) data are accurate enough to ground answers.
→Agent design
Define topics, instructions, guardrails and the hand-off to a human, written so business owners can review them.
Actions
Build the actions the agent calls, using Flow, Apex invocable methods or prompt templates, each with tight input checks.
→Testing and evaluation
Build test sets of real-world requests, including awkward and adversarial ones, and run them before every change.
→Monitoring and iteration
Review conversations, hand-off reasons and action failures, then adjust instructions and actions.
→How an engagement runs
- 01
Discovery
Workshops with the business owner and platform team to list candidate use cases, volumes and the data each needs.
- 02
Readiness review
We look at the org: data quality on the relevant objects, knowledge content, sharing model, existing automation, and which users and permissions the agent would run under.
- 03
Design
One use case, written down as topics, instructions, actions, escalation rules and examples of good and bad responses. The business owner signs this off.
- 04
Build in a sandbox
Actions, prompt templates and agent configuration built and version-controlled, with the same deployment process as the rest of your org.
- 05
Test and pilot
Run the test set, fix what fails, then pilot with internal users or a small share of real traffic with close review.
- 06
Launch and review
Wider rollout with agreed monitoring, plus a review after the first weeks to decide what to improve and whether to start the next use case.
What you receive
- A use-case shortlist with a clear recommendation and the reasons for it.
- A readiness report covering data, knowledge, permissions and automation that the agent depends on.
- A written agent design: topics, instructions, actions, guardrails and escalation paths.
- Built and tested actions in your org, deployed through your normal release process.
- A reusable test set and an evaluation routine you can run after each change.
- A monitoring checklist and runbook for the agent's owners.
Risks and what we need from you
Agents amplify whatever they are connected to. Out-of-date knowledge gets repeated confidently, and an agent sees whatever its running user can see. So before any build we check:
- Permissions: the agent's running user and the actions it can invoke follow least privilege. Customer-facing agents get access only to the records of the person they are helping.
- Data protection: Salesforce's Einstein Trust Layer provides controls such as data masking and audit for generative features. Review what it covers for your org and your use case with your security team, rather than assuming.
- Consumption and cost: Agentforce usage is metered, and pricing models vary by contract. Confirm the commercial position with your Salesforce account team before scaling.
- Ownership: someone in the business must own the agent's instructions and review its conversations. Without that, quality drifts.
From you we need a named business owner, access to a sandbox that reflects production data shape, examples of real requests (anonymised where needed), and time from the people who handle those requests today.
Questions we hear
Do we need Data Cloud to use Agentforce?
Can an agent update records, or only answer questions?
How do you test something that does not give the same answer twice?
What if our org has a lot of old automation?
Talk to us about Agentforce
If you have a use case in mind, or have been asked to evaluate Agentforce, we can start with a readiness review of your org and the use case.
