AI Agents
Production agents, not demos.
Anyone can wire a language model to an API. The hard part is an agent your team can rely on: scoped correctly, integrated with your systems, monitored, and safe to run without supervision. That is what SYN.AUTON builds.
What we build
Account research agents
Agents that research target accounts across public and internal sources and write structured briefs into your CRM before the first touch.
Pipeline inspection agents
Agents that review deals continuously and flag risk, gaps, and next actions to owners.
Operations agents
Agents for routing, enrichment, data hygiene, and internal Q&A, scoped to defined bounds with human review where it matters.
Agentic workflow design
Architecture for where agents belong in your processes, where humans stay in the loop, and how it all gets monitored.
Further reading
- Guardrails for AI agents that write to your CRM
An agent with write access to your CRM can quietly corrupt the system of record. These are the guardrails that make it safe: scoped permissions, trust earned per field, audit trails, and a kill switch.
- How to implement a policy layer between an AI agent and Salesforce
A policy layer sits between the agent and the API and decides whether each write is allowed before it happens. Here is what it evaluates, where it runs, and how to roll it out without blocking real work.
- What to log when an AI agent writes to your CRM
If an agent writes a wrong value to four thousand records, the log is the only thing that tells you which run did it and what the values were before. Here are the fields that log needs.
- Agentforce vs a custom AI agent
Agentforce is the right answer when the work lives inside Salesforce and looks like what Salesforce modeled. A custom agent wins when the work crosses systems or the logic is yours. We build the second, so read the first section carefully.
Have a ai agents problem?
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