AI Agent Development
We build bespoke autonomous AI agents and deploy/customise open-source frameworks: OpenClaw, Hermes, and Claude Code, tuned for your stack, data, and operations. Self-hosted on-prem deployments available for regulated industries.
The value is in agents that act, not chat — ones that execute inside your tools rather than answer questions about them. So we architect around your SOPs, tools and escalation rules, and you own the result end to end. For regulated and high-trust teams, sending sensitive operations through external APIs is a non-starter, which is why on-prem deployment is a first-class option rather than an upsell.
Engagements cover agent architecture and tool design for your stack, framework selection (custom vs OpenClaw vs Hermes vs Claude Code), private data and RAG over your internal knowledge, self-hosted or on-prem deployment, evals, observability, audit logs and prompt versioning, and ongoing tuning, guardrails and capability rollouts. Typical work includes internal dev and ops automation, document processing and extraction, and customer-facing agents on the channels you already use.
Related: voice phone agents for the telephone channel, Genny for WhatsApp, and SOP automation when the process matters more than the conversation. Book a scoping call.
Frequently Asked Questions
What is an AI agent?
An AI agent is a software system that autonomously executes tasks inside real tools and workflows, rather than a chatbot that just answers questions about them. Genium builds bespoke agents and also deploys open-source frameworks — OpenClaw, Hermes, and Claude Code — configured to a client's stack, data, and SOPs, with self-hosted or on-prem deployment available for regulated industries.
What exactly does your AI agent do?
Genium's AI agents execute inside a business's existing tools and processes — typical work includes internal dev and ops automation, document processing and extraction, and customer-facing agents on channels the business already uses. Each engagement covers agent architecture and tool design, framework selection (custom vs. OpenClaw vs. Hermes vs. Claude Code), private data/RAG over internal knowledge, and deployment, with escalation rules built around the client's own SOPs.
How do you ensure the AI works in production, at scale?
Genium builds evals, observability, audit logs, and prompt versioning into every agent engagement, so behaviour can be measured and corrected after go-live rather than assumed to work from a demo. Ongoing tuning, guardrails, and staged capability rollouts are part of the engagement, not a one-off deployment step.
How will your solution protect our data (and our customers' data)?
Genium offers self-hosted, on-prem deployment as a first-class option because sending sensitive operations through external APIs is a non-starter for regulated and high-trust teams. Agents are architected around the client's own tools, data, and SOPs, and the client owns the result end to end rather than routing operations through third-party services by default.
What are the limitations of AI agents, and how can users minimize the impact?
An AI agent's limitations come from the tools, data access, and SOPs it's given — it can only act reliably within the scope it was architected for, and gaps in escalation rules or tool integration surface as errors or stalled tasks. Genium reduces this risk by designing escalation rules, guardrails, and phased capability rollouts into each engagement rather than granting an agent unlimited scope from day one.
How is an AI agent evaluated? What metrics are used to measure performance?
AI agent performance is assessed through evals, observability, and audit logs that record what the agent did, why, and whether it matched the intended process. Genium includes these alongside prompt versioning in engagements, so performance is checked against defined tasks and SOPs rather than judged only on subjective chat quality.