We often hear from customers that the challenge with implementing AI in customer operations is less about the technology itself, and increasingly about the team structure and processes around it. We work with clients who have spent years getting their stack right, from building custom CRMs to maintaining massive knowledge bases that the operations teams are constantly updating. Whatever the configuration may be, your AI agent has to live inside it.
Some teams are happy running our AI agent inside their existing support desk. In that setup, the agent slots in alongside your human reps and works in the same surface they do. Other teams want to bring the agent into their own backend, run it on the channels they've built themselves, and manage it through code.
We support this through our Conversations API, which makes our agents headless. "Headless" means the engine of a system is decoupled from its front end, so the team using it can plug their own surface on top. Headless agents work the same way.
In this setup, your platform stays in charge of the customer experience, while Gradient Labs handles the agent logic, routing, and tooling underneath. The conversational experiences and operational workflows your team has refined for years stay where they are, with the agent working inside them.
How does a headless agent work?
A headless agent has no UI of its own. Customers see your product, whether that's an in-app chat, a phone line, or an email thread. Behind that surface, the Gradient Labs agent reads the conversation, follows your procedures, calls your tools, and either replies or hands off to a human.
In practice: your backend creates the conversation when a customer opens your in-app chat or calls your support line, and forwards each new message to the AI agent. The agent's replies come back to your system through webhooks, ready for you to render in your own surface.
This works for frontline support and proactive outreach. The same API that powers a customer-initiated chat about a missed payment also supports an agent-initiated voice call to a customer in early arrears. Our Lending Agent runs on this pattern in production today.
As always, Gradient Labs agents work alongside your human team. The same conversation is shared between agent and rep, so a handover doesn't require a separate tool or a copy-paste of context. A rep can be shown what the agent has done so far and pick up where it left off. The agent can hand back when it should, or ask a human for help mid-conversation when it needs guidance from a colleague.
Your agent-management layer
Beyond running conversations, the Conversations API gives engineers control over how the agent works over time. Through the same API, you can:
Add and update knowledge as it changes in your business
Set procedure rollout limits and run experiments on variants
Register new tools and resources the agent can use
Automate housekeeping work like notes and knowledge syncing
Knowledge syncing is a common starting point. One of our customers publishes their internal content in a CMS their ops team has been writing into for years. Their engineers wired that CMS into Gradient Labs' public API surface. Now every article their wider team writes or updates flows straight into the agent. The work happening across the business levels up the agent, automatically.
Procedures, the natural language instructions our agents follow, work the same way. You can gate a new procedure version, cap how many conversations use it per day, let it bake, then promote it. You can run two variants and read the difference in resolution. We've covered the knowledge architecture this sits on top of in this article about why your knowledge base isn't enough.
Agents plug into your existing tooling and guardrails
If you've built sophisticated internal tooling, the Conversations API lets you plug Gradient Labs into it. The custom tools you register become things the agent can call as part of its reasoning, whether that's a fraud-scoring service or an internal CRM lookup. You control what they expose and what they return. The agent decides when to reach for them.
Guardrails are where this matters most. Our default layer is comprehensive: agent guardrails that keep the agent inside what it's authorised to do, and customer guardrails that watch for vulnerable-customer signals and compliance triggers as the conversation unfolds. Both are purpose-built for financial services, tuned over the last few years against the regulatory landscape we work in, including FCA Consumer Duty and the EU AI Act.
Many of our customers run on those defaults alone, but others have spent years building their own internal guardrails and want to layer those on top of ours. This could be a custom vocabulary list or an internal escalation rule that fires when a customer mentions a specific product. Because agent messages and handover events flow through your backend, those checks can run before anything reaches the customer, without forking our agent.
The future of agentic operations is headless
We're seeing more financial services teams run headless agents, inside the apps and systems they've already built. It's how the largest deployments on our platform already operate today.
If that's how you want your AI agent to operate, book a demo and we'll help you get started.
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