Agent adoption is moving faster than enterprise readiness
McKinsey’s 2025 survey found 62% of respondents were at least experimenting with AI agents. Cambridge’s 2026 global financial-services study reported active agentic-AI adoption among 52% of industry respondents, while only 23% were at the more mature scaling or transforming stages (McKinsey, 2025; CCAF, 2026).
The gap matters because an agent is not simply a chatbot with a longer prompt. It can plan, retrieve information, invoke tools and execute multi-step workflows. Every added action surface creates an authority, security and accountability question.
The architecture must encode what the agent is allowed to do
Agentic design needs explicit boundaries around identity, entitlements, tool access, transaction limits, data access, approval points and termination conditions. These controls should not be hidden inside natural-language prompts.
An enterprise agent should be able to prove which evidence it used, which policy applied, which tool it called, who authorised the action and when human review was required. That makes decision architecture as important as model architecture.
- 01Intent
- 02Context
- 03Authority
- 04Tool call
- 05Runtime control
- 06Action
- 07Escalation / audit
Autonomy should increase only when evidence, authority and controls support it.
Governance has to move into runtime
The Financial Stability Board’s 2026 consultation on responsible AI adoption proposes organisation-wide governance practices across the AI lifecycle. For agentic systems, lifecycle governance is necessary but not sufficient: controls must also operate while the agent is executing (FSB, 2026).
A useful control pattern is progressive autonomy. Low-risk, reversible actions can operate with broader automation; high-impact actions involving money, legal rights, sensitive data or external commitments should carry tighter limits and explicit approval paths.
Arvec view: readiness is a system of capabilities
Agentic readiness should therefore be assessed across strategy, operating model, governance, engineering, data/knowledge and human capability—not through a single technical prototype.
Argument Vectors provide a complementary decision-level structure: intent, context, constraints, evidence, authority, tools and objective. AIF7™ then asks whether the enterprise has the capability to execute that decision structure reliably at scale.
Key implications
- 01
Treat agent identity and authority as architecture, not prompt engineering.
- 02
Govern execution trajectories and tool calls, not only final outputs.
- 03
Adopt progressive autonomy based on action risk and reversibility.
- 04
Assess knowledge, controls, operating model and escalation before scaling agents.
Related AIF7™ capabilities
AI Governance & Trust
Defines runtime authority, controls and accountability.
AI Engineering & Delivery
Implements tool gateways, observability, evaluation and containment.
AI Data Foundation
Provides trusted context and evidence for agent decisions.
AI People & Culture
Defines human supervision, escalation and changed roles.
Sources & further reading
01 · McKinsey 2025
The state of AI in 2025: Agents, innovation, and transformationMcKinsey & Company · 5 November 2025
02 · CCAF 2026
2026 Global AI in Financial Services Report – Adoption, Impact and RisksCambridge Centre for Alternative Finance · 2026
03 · FSB 2026
Sound Practices for Responsible Adoption of Artificial Intelligence: Consultation reportFinancial Stability Board · 10 June 2026