01 — Runtime
AI agents
Get agents into real work — not stuck in a chat box.
Enterprises do not need another conversational skin. They need systems that complete work inside permission: query knowledge, change tickets, trigger workflows, draft decisions, and hand the result back for review.
We design agents as backend services — tool boundaries, observable traces, revocable authority, replayable audit. The model is one layer. Multi-agent collaboration, memory, planning and the human loop sit on the same path to production.
In practice
Tools and orchestration
Internal APIs, tickets, knowledge and approval become tools — with allow, deny and escalation paths.
Human in the loop
High-risk steps stay with people. Agents gather, draft and execute the low-risk path; decision rights can be pulled up by policy.
Runtime observability
Prompts, tool calls, failures, latency and cost are traceable. When something breaks, you can locate it.