AI agents at AptixLabs are application-specific, not generic chatbots. The studio builds them around a clear job-to-be-done — onboarding a new gym member, answering questions about a programme, generating a workout from a template — and grounds each agent in the client's own Firestore data via tool calls.
Model routing
The agent runtime is built on the Anthropic and OpenAI APIs depending on the strengths needed; for cost-sensitive paths the studio routes to Gemini via Vertex AI. A short classification call goes to Claude Haiku or Gemini Flash; a long-form coaching response goes to Sonnet or GPT-4-class only when the quality difference justifies the cost.
Where the agent runs
Agents run inside Cloud Run and stream responses to the client over server-sent events. State is persisted in Firestore so a user can return to the same conversation across devices. Every tool call is logged for debugging and replay.
The guardrails layer
A guardrails layer enforces what the agent can and cannot do. An agent helping a fitness member can never accidentally write to billing or admin data — the available tools are explicitly scoped per agent, and the rule surface is reviewed before every release. The guardrails also catch prompt injection: if the user message contains content that looks like a system instruction, the agent refuses.
How we test agents
- A fixed evaluation set of 50 representative prompts for every agent
- Automated regression: every model upgrade re-runs the eval set
- Manual exploratory testing on edge cases
- Production trace sampling for the first week after any model change