Topic starter
18/08/2026 2:56 pm
TL;DR: Agentic AI is shifting code execution from reviewed pipelines into runtime workflows, where agents generate logic, select tools, and act with broad permissions, according to Apiiro. That makes execution risk less about developer-written code alone and more about autonomy, context, and control of live execution paths.
Editorial analysis by NHI Mgmt Group, based on content published by Apiiro: “The Top Code Execution Risks in Agentic AI Systems in 2026”.
By the numbers:
- 33% of enterprise software applications will use agentic AI by 2028, according to Gartner cited by Apiiro.
- roughly 15% of day-to-day work decisions made autonomously, according to Gartner cited by Apiiro.
Key questions
Q: What breaks when agentic AI can generate and run code at runtime?
A: The main failure is the loss of a clean separation between development review and execution.
Q: Why do broad agent permissions increase security risk?
A: Broad permissions increase risk because one compromise or misconfiguration can expose many systems at once.
Q: What do security teams get wrong about agentic code scanning?
A: The common mistake is treating the model as the control, when the harness is what actually drives precision, scope and budget.
Practitioner guidance
- Define execution boundaries for every agent Separate agents that can only recommend actions from those that can invoke interpreters, shells, or cloud APIs.
- Restrict context sources before execution Block untrusted documents, logs, and retrieved memory from reaching code-generation or tool-invocation steps unless they are explicitly validated.
- Review service-level permissions for agent workflows Inventory every agent that runs with service-level credentials and map the exact systems it can touch during a task.
Bottom line: Agentic AI shifts code execution from reviewed pipelines into live workflows, where context and tool choice become security controls.
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18/08/2026 3:39 pm
Runtime execution is now an identity problem as much as an application problem: once agents can generate and run code inside production workflows, the decisive control becomes who or what is allowed to execute at that moment. Static review still matters, but it no longer defines the full attack surface. Practitioners need to govern execution authority, not just code provenance.
A few things that frame the scale:
- 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey.
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should organisations govern agentic AI when it makes judgment calls, not just automated actions?
A: Organisations should govern the decisions agentic AI is permitted to make, not only the data it can access. That means defining escalation thresholds, preserving human decision rights for ambiguous cases, and logging the signals that explain why the agent chose a path. If the decision itself is unreviewable, policy drift can occur without a visible breach.
👉 Read our full editorial: Agentic AI code execution risk is moving into runtime workflows