A technical overview of the terranoha.ai platform: agents, orchestration, knowledge grounding, security, observability, evaluation, and deployment options.

Platform

Overview

Terranoha.ai provides the engineering building blocks required to deploy AI agents in production environments: governed tool access, reliable orchestration, verifiable knowledge grounding, and an operational layer for security, observability, and continuous evaluation.

Platform pillars

  • Agents: goal-driven systems that can plan, act, verify, and escalate.
  • Orchestration: stateful workflows with retries, approvals, and SLA awareness.
  • Knowledge layer: ingestion and retrieval with citations and permission enforcement.
  • Model layer: model routing, embeddings, and cost/latency controls.
  • Security: least privilege, tenant isolation, audit trails, and safe-by-default policies.
  • Observability: traces, metrics, quality signals, and cost attribution.
  • Evaluation: repeatable test harnesses and regression tracking for quality.
  • Deployment: cloud, hybrid, and enterprise integration patterns.

How to navigate

Start with /platform/agents to understand the runtime behavior, then /platform/orchestration for reliability, and /platform/knowledge for grounding and data access. Engineering implementation patterns live under /engineering and API-level details under /docs.