Einstein Bots were rule-driven and required scripting every flow. Agentforce uses the Atlas Reasoning Engine to plan multi-step actions inside topic boundaries you define. The build pattern is closer to designing an agent contract than scripting a chatbot.
Not strictly, but practically yes. Agentforce performs much better when grounded in Data Cloud. We typically build Data Cloud foundation first, then layer Agentforce on top.
We build a test suite of grounded scenarios, run them on every change, score task completion and hallucination rate, and gate launch on a defined threshold. Evaluation is not optional.
A bounded first agent (Service Agent on a defined intent set) runs 10 to 14 weeks including Data Cloud grounding and evaluation. A multi-agent enterprise deployment runs 6 to 9 months.
We build a Service Agent grounded in your knowledge base and Service Cloud history. Bounded scope, evaluated, with handoff rules to humans.
Sales Agent grounded in Sales Cloud + Data Cloud. Drafts follow-ups, surfaces stale opportunities, suggests next-best-actions, all auditable.
Most Agentforce pilots fail on evaluation. We build the test suite, regression tests, and acceptance gates so the agent does not regress in production.
Once the agent is live, drift, data changes, and topic expansion all create maintenance work. We run that ongoing operation.
Agentforce reads from Data Cloud. We design and clean the Data Cloud layer first so the agent has trustworthy inputs.
Most Agentforce SIs are CRM consultants who learned agents. We are data engineers and data foundation consultants who learned Agentforce. That sequence matters.
Agents fail when they are unbounded and unevaluated. Every Thinklytics agent has scoped topics, action allowlists, and a regression suite.
We do not hand off and disappear. Drift, topic expansion, and tuning are part of the ongoing engagement.
Policies, monitoring, and ongoing tuning after the agent is live.
Agentforce consulting: Agentforce is only as smart as the data it reads from and the topics and actions...
Data Cloud grounding so the agent reads from clean, governed data.
Managed operations covering monitoring, drift detection, and ongoing tuning.
A demo build. We deliver agents that survive contact with real customers.
A licensing pitch. We tell you when Agentforce is not the right tool.
A black-box agent. Every topic, action, and instruction is documented and reviewable.
Agentforce agents are only as good as the data they read. These are the factors that move the effort.
Agents need trustworthy, governed data to ground on; ungoverned data needs work first.
Each agent topic and action you enable adds design and testing.
Connecting agents to your data, knowledge, and external systems adds work.
Approval gates, access controls, and audit on agent actions are part of safe deployment.
You run Salesforce and want Agentforce grounded in trustworthy data.
You need agents that act safely, with access controls and audit.
Pilots stall because the data underneath the agent is messy.
You want agents built outside Salesforce on your own stack: see AI Agent Consulting.
Your data is not unified yet: see Salesforce Data Cloud Consulting or Data Foundation.
You want AI governance operated for you: see AI Governance & Managed Operations.
Agentforce is only as smart as the data it reads from and the topics and actions it is grounded in. We come from the data foundation side, which is the part of the build that sinks most Agentforce pilots. Get the data layer right, the rest is engineering.
Salesforce Agentforce consulting stands up Agentforce agents grounded in clean data, topics, and actions. Agentforce is only as smart as the data it reads from, and the data layer is the part that sinks most pilots. Thinklytics comes from the data foundation side, gets that layer right first, then handles the agent engineering.
Start with an Analytics Truth Audit. We assess your Salesforce data layer, define the agent scope, and give you a 90-day Agentforce plan.