Are all capabilities needed from day one?
Identity, logging and the model gateway are needed early. Other capabilities can grow with the number of use cases.
AI Orchestration Platform · Capabilities
Capabilities an AI Orchestration Platform Must Deliver
What each capability means in practice and which questions reveal whether a platform delivers it in production quality.
Model gateway
A single entry point for all model calls with authentication, routing, rate limiting, caching and logging. Ask how quickly new models are supported and how routing rules are defined and tested.
Knowledge retrieval
Connectors to your document systems, indexing with metadata, permission enforcement at query time and source citations in answers. Ask how permissions stay synchronised when they change in the source system.
Agent runtime
Definition of agents with tools, budgets and approval rules, plus traces of every step. Ask how the platform stops runaway agents and how approvals are presented to users.
Workflow engine
Deterministic steps, branching, retries, schedules and human tasks combined with AI steps. Ask whether long-running processes survive restarts and how state is stored.
Governance
Policies per data class, model allow-lists, retention settings and exportable audit logs. Ask for an example audit trail of a single request.
Evaluation
Test sets per use case, automated scoring, human review workflows and comparison of model or prompt versions. Ask how regressions are detected before release.
Cost control
Budgets per team and use case, alerts and reporting by model and feature. Ask whether costs can be attributed to individual processes.
Frequently asked questions
Can a workflow tool with AI steps replace a platform?
For simple automations, yes. For agents, retrieval with permissions and central governance across many use cases, a dedicated layer is usually needed.