Topic starter
20/08/2026 5:34 pm
TL;DR: Agents are moving into workflows faster than governance, with roughly 30% already integrated and about 61% expected to handle half of jobs within three years, according to TruFoundry. Agent governance now has to cover tool access and runtime control because delegated action changes how systems are approved and audited.
Editorial analysis by NHI Mgmt Group, based on content published by TruFoundry: “BCG Says Strategy Matters More Than Tools — Part 2: From Agent Adoption to Governed Tools and Runtimes”.
By the numbers:
- around 30% of respondents say their organizations have integrated AI agents into workflows
- about 61% expect agents to handle half their job within three years
Key questions
A: SaaS AI agents can operate across live systems and inherit broad access from existing users, features, or auto-configured modules.
Q: How do security teams know if agent governance is actually working?
A: It is working only if the team can answer three questions quickly for any agent: what it can reach, what it did recently, and whether that behaviour matches intent.
Q: What breaks when an AI agent is allowed to call tools without strict scope controls?
A: The main failure is privilege expansion.
Practitioner guidance
- Define separate controls for model access and tool access Map which permissions belong to the model layer, which belong to MCP tool calls, and which must never be embedded in agent definitions.
- Move sensitive actions behind runtime approval gates Require explicit human authorization for high-impact steps such as external writes, deletions, payments, or policy changes before the agent can continue.
- Require per-step traces for every governed agent Capture tool name, execution step, decision point, and outcome so investigators can reconstruct the run without relying on prompts alone.
Bottom line: Agents change the control problem from prompt supervision to delegated action governance across tools and runtimes.
Glossary terms: Agent Runtime, Tool Governance, Observable Execution, Delegated action
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This topic was modified 2 days ago by NHI Mgmt Group
20/08/2026 6:20 pm
AI gateway governance is now an access-control problem, not just a model-routing problem. Once agents can call tools and act inside a runtime, the security question shifts from what the model said to what the system was allowed to do. That is why model-centric controls are insufficient on their own. Practitioners should treat delegated action as a distinct governance surface, not a side effect of chat.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
A question worth separating out:
Q: Should organisations treat agent runtimes as part of identity governance?
A: Yes. Once the runtime decides when an agent executes, pauses, or resumes, it becomes part of delegated access control. Identity governance has to extend to the loop that carries the action, because the runtime is where authority is actually exercised.
👉 Read our full editorial: AI gateway governance is widening to cover agent tools and runtimes