AI-native architecture is a system design in which structured context, AI agents and workflows are core primitives of the platform, rather than features added to an existing product. Sonta AI is the agentic implementation of it: in an AI-native CRM, what the model sees is chosen, not scraped (typed fields, field groups and a scope you set per collection), so every answer can be checked against the record it came from.
The server picks the meaningful fields automatically. Turn off to choose manually.
Context is the structured record every agent reads. Agents are configured AI workers with goals and models. Workflows are the processes that string them together.
A workflow is a trigger and a series of steps, some of them agents. Branches carry conditions, each step records what it did, and a completed run can be replayed exactly as it ran.
Eight ready-made agents to start from, and a dozen triggers when you build from scratch. Every recipe opens in the builder.
An agent is a goal, a model, and a place to put the answer. The goal is plain language. The model is a select: Claude, ChatGPT, Gemini or Perplexity, chosen per task. The answer lands in named fields, so the next step reads structure rather than prose.
One panel holds what an insight or an agent is allowed to do: focus and length presets, the connection and model, the prompt and system prompt in plain text, the answer format, and the fields it may read. Nothing here is a hidden default someone has to guess at.
Set per collection, so a support record and a deal record can read differently without either being rewritten in code.
Frontier and open source, side by side. The model is chosen per task: inside one workflow, different agents run on different models.
Builder agents shape the workspace in conversation. Name the collection, the fields, the layout, and the builder creates it, linked to the objects you already have. The same path an admin uses on day 200 is the path that built the demo workspace on day one.
Structural facts only. Each rail below is a surface in the product, not a policy page.
Each rail is a surface in the product. A workflow rehearses before it runs, passes its checks before it turns on, and keeps a record of every run afterwards.
Rehearse the workflow against real records. Nothing sends.
Trigger and steps verified before a workflow can be turned on.
Every run kept, step by step, completed and failed alike.
Failed steps retry on a set schedule before they escalate to a person.
A failing step halts what depends on it instead of running on stale output.
Field history versions every record: compare to current, restore any version.
Simulates the agent without firing side effects. Actions return stubbed outputs; delays are skipped.
Drafts wait in a review queue. Where review is switched on, a send needs a person's approval, and approval tasks are created and assigned with the brief attached.
New agents run with output visible and nothing sending, until the team turns each workflow live.
No training on customer data. Encryption at rest and in transit, role-based access, audit trails. GDPR-aligned by design (see privacy).
AI-native architecture is a system design in which structured context, AI agents and workflows are core primitives of the platform rather than features added to an existing product. In a CRM, that means the data model is built for agents to read and write in real time, every AI output is grounded in named records, and the whole platform is operable by AI through open standards such as the Model Context Protocol.
An agentic CRM is built so agents can execute the work: qualify, draft, brief, update. A CRM with AI features adds assistance on top of a data model designed for manual entry, so the AI works on whatever reps typed in. The structural difference is where the work happens: inside the record, with provenance, or beside it.
No. In an AI-native architecture the model layer is replaceable by design. Your context, agents and processes live at the platform layer, and each workflow step names its model, so when the best-fit model changes the step is re-pointed in a setting. Work runs continuously while the model layer evolves underneath.
No. Sonta AI does not train models on customer data. Encryption at rest and in transit, role-based access and audit trails are part of the platform, and never-log rules and suppressions govern what agents may store.
Bring a workflow you already run. We'll set it up as an agent in Sonta AI and show you every setting behind it.