AI Agent Engineering & Copilot
Agentforce and Einstein set up to handle routine requests inside Salesforce, with firm limits on what the AI may do and a person to catch the rest.
- Typical timeline
- 6 to 12 weeks
- Delivery team
- AI architect, developer, admin
- Best for
- Teams with a high volume of repeat service or sales requests.
Sound familiar?
- Agents spend hours on order status and password questions.
- The chatbot cannot see the customer's record, so all it can say is "please hold".
- Legal wants to know where the data goes before anyone touches AI.
What we actually do
We start with your case data to find the two or three request types worth automating. Then we build an agent that does specific things, like looking up an order, updating a case or booking a callback, and stops when a request falls outside that. Results depend on your data quality, so we measure on a pilot group before going wider.
What you walk away with
- A review of which requests are worth automating
- Prompt templates grounded in your own CRM records
- Agent topics, actions and guardrails
- Hand-off rules and a dashboard to watch quality
- A test plan covering accuracy, edge cases and safety
Einstein Copilot for your data model
Prompt templates written against your own objects and fields, so the answers point back to real records.
Copilot guideAgents that take action
Automation inside Service and Commerce Cloud with hard limits on what the agent can change.
Agentforce guideUnlocking Salesforce Agentforce Blueprint
Detailed architecture guide on autonomous agents, metadata-grounded actions, and LLM trust boundaries.
Read Agentforce BlueprintHow a project runs
- 1
Find the use cases
Rank requests by volume and risk, then agree what success looks like.
- 2
Build
Topics, prompts and the CRM actions the agent is allowed to take.
- 3
Try to break it
Test odd questions, missing data and attempts to get around the rules.
- 4
Pilot
Launch to a small group, review conversations weekly, then widen.