Framework Dimension 6 of 6

Agentic AI

Tool use via standardized protocols such as the Model Context Protocol, multi-agent orchestration, agent evals, and software- or browser-operating agents.

The Agentic AI dimension measures whether you are running autonomous, tool-using agents in production with proper evaluation and guardrails — or whether "agentic" is still a term on a slide deck.

Framework v2026.1 · Updated · machine-readable spec

Why this dimension matters

Agentic AI changes the failure surface. Standard LLM evals do not catch runaway loops, cascading tool errors, or prompt-injection escalation through tool outputs. Organizations that ship agents without an agentic-specific eval harness and runtime guardrails experience production incidents at 5-10× the rate of orgs with them.

Signals we look for

  • Standardised tool-use protocol adopted (e.g. Model Context Protocol)
  • Agent evaluation harness distinct from LLM evals
  • Runtime guardrails specific to autonomous execution
  • Selective use of reasoning models for planning-heavy subtasks

What this dimension looks like at each maturity level

  1. No agentic systems in production. Some experimentation with prompt chains.

  2. First agent prototypes using off-the-shelf frameworks. No formal evaluation.

  3. At least one agent in production with runtime guardrails and basic task-success metrics.

  4. Standardized tool-use protocol (Model Context Protocol) adopted across multiple agents. Agent eval harness distinct from LLM evals.

  5. Planning-capable reasoning models drive dynamic tool selection. Continuous evaluation auto-improves prompts and workflows.

  6. Fleet of collaborating agents including software- and browser-operating agents. Multi-agent orchestration is a first-class engineering discipline.

Common blockers

  • Building agents on ad-hoc tool integrations rather than adopting MCP or an equivalent protocol.
  • Using LLM evals for agents — missing trajectory quality, tool-call correctness, and cost-to-solution.
  • No per-agent cost budget or step limit — first incident is a runaway loop.

How to move up a level

  • Adopt Model Context Protocol (or equivalent) for all agent tool integrations.
  • Build an agent eval harness that measures task-success rate, trajectory quality, and cost-to-solution.
  • Impose per-agent budgets: max steps, max tool calls, max cost per task.
  • Add human-in-the-loop checkpoints for irreversible actions (send email, transfer funds, delete data).

Related standards & regulation

For developers

Call this framework and its tools from your own agent via the Model Context Protocol (MCP) server. Works with Claude Desktop, Cursor, Zed, Continue, and the OpenAI Agents SDK.