Topic starter
24/06/2026 8:40 pm
TL;DR: 90% of enterprises are adopting AI agents and 79% expect full production rollout within three years, according to Kong. The real shift is that AI connectivity is becoming a control problem, not just an integration problem, and identity governance has to catch up, while 72% are already implementing formal governance layers.
Editorial analysis by NHI Mgmt Group, based on content published by Kong: “# The 2025 Kong Year in Review”.
By the numbers:
- 90% of enterprises are adopting AI agents, according to Kong research.
- 79% expect full production rollout within three years, according to Kong research.
- 72% of organisations are already implementing formal governance layers, according to Kong research.
Key questions
Q: What breaks when AI connectivity is added without policy controls?
A: Teams end up with hidden trust edges, overbroad service access, and logs that do not explain which workflow triggered the action.
Q: When should teams prioritise governance over broader agent rollouts?
A: Teams should prioritise governance before scaling agent use whenever the system can act on production data, invoke tools, or affect downstream workflows.
Q: How do teams know whether an AI gateway is actually improving control?
A: They should measure whether the gateway reduces unmanaged keys, improves audit completeness, and enforces consistent access rules across all providers.
Practitioner guidance
- Define an AI connectivity governance boundary Inventory the APIs, models, agents and event streams that participate in agentic workflows, then assign a single policy owner for each boundary and escalation path.
- Classify token budgets as access controls Set budget thresholds, approval rules and exception handling so that model usage limits are enforced alongside authorisation, not after the fact.
- Map connector authentication to delegated trust Document which connector uses direct service credentials, which uses delegated scopes and which can call downstream services without human intervention.
Bottom line: AI connectivity is becoming a governance layer because agentic systems now drive API, model and event traffic at machine speed.
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25/06/2026 5:23 am
AI connectivity is becoming the governance layer that sits above modern machine-to-machine access. Once APIs, LLMs, agents and event streams are handled together, the enterprise is no longer governing isolated connections. It is governing a shared policy surface where identity, usage and risk are decided in motion. The practitioner takeaway is that AI connectivity now belongs in the same governance conversation as workload identity and privileged access.
A question worth separating out:
Q: What is the difference between API governance and AI gateway governance?
A: API governance focuses on standardizing how services expose and consume APIs, including versioning, authentication, rate limiting, and access control. AI gateway governance applies the same discipline to LLM calls, MCP tool access, and agent-to-agent communication. The difference is the traffic type and the controls it needs, especially token awareness, prompt security, model routing, and agent identity management.
👉 Read our full editorial: Kong's 2025 year in review shows the rise of AI connectivity