Govern them as delegated executors, not as standalone users. That means proving the human at invocation, issuing only the scopes needed for that run, logging both the grant and the action, and revoking the connection when access should end. The control objective is accountability across the entire delegation chain.
How to Govern AI Coding Agents as Delegated Executors
AI coding agents should be governed as delegated executors, not as standalone users with open-ended standing access. The IAM question is not whether the agent can act, but under what human-approved delegation, for which scope, and for how long. That framing keeps ownership, accountability, and revocation attached to the invocation rather than to a vague “agent identity.”
Across GitHub, MCP tools, and connected systems, the right control model is task-scoped authorization. Prove who invoked the agent, constrain the permissions to the specific run, and treat the resulting actions as attributable to that human and that delegated session. This is the same reason teams managing AI agent authorization need per-action decisions instead of broad evergreen access.
That also means the policy boundary has to span the whole execution path. A GitHub workflow token, an MCP server credential, and a cloud API key may all participate in one delegated activity, so the governance model must follow the chain end to end. AI Agent Identity Security: The 2026 Deployment Guide is useful here because it treats identity, scope, and short-lived access as the operational core of agent control.
What Good Delegation Looks Like Across GitHub and MCP
A practical governance pattern starts with human verification at invocation, then issues a narrowly bounded token or session for the task. The agent should inherit only the minimum permissions needed to open a pull request, read a repo, call a tool, or write to a specific workspace, and nothing more. That is especially important when the agent can chain tools, because the effective privilege can expand faster than the original request looks on paper.
Governance should also distinguish between access that is merely available and access that is actually usable in the current context. If the agent can reach GitHub, an MCP server, and a deployment system, the team should still decide whether those systems may be combined in one run, because cross-system chaining is where accidental or malicious overreach tends to appear. NHIMG’s AI Coding Agents Security Guide is a helpful reference for this tool-chain and secrets-in-context problem.
Revocation matters as much as initial approval. When the task ends, the connection should be withdrawn, the session invalidated, and any cached or delegated capability removed so the agent cannot keep acting on stale trust. For long-running workflows, the safest pattern is re-approval for a new run rather than quietly extending the old one.
Why Delegation Controls Fail in Practice
The main failure mode is treating an agent like a normal developer account and forgetting that its speed, breadth of tool access, and ability to follow instructions make small permission mistakes much more dangerous. If the agent can read secrets, call external tools, or push code without per-run boundaries, it can turn a single exposed token into a broad action path. The risk is not just misuse, but invisible propagation of privilege through connected systems.
Another common failure is incomplete logging. If teams log the agent’s actions but not the human request that authorised them, or log the approval without the downstream API calls, they lose the audit chain needed for investigation and accountability. That gap matters most when an action spans GitHub, MCP, and infrastructure systems, because the evidence is otherwise scattered across separate control planes.
Finally, standing access creates drift. A permission that was defensible for one repository or one tool often gets reused for a different task, then survives because no one re-evaluates it. That is how delegated execution slowly becomes permanent privilege.
Risk and Threat Considerations
AI coding agents combine human intent, machine speed, and tool access, so mistakes in delegation can quickly become security incidents. The highest-risk conditions are over-scoped tokens, weak approval checks, reusable credentials, and MCP tools that can reach more systems than the original task required.
Failure mechanism: An attacker, prompt injection, or accidental misuse exploits standing permission, broad tool scopes, or unlogged delegation to make the agent perform actions the human did not intend or cannot later prove.
Impact: That can lead to code changes, secret exposure, data deletion, lateral access, or unauthorised infrastructure actions with poor attribution and delayed containment.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and OWASP API Security Top 10 address the attack and risk surface, while NIST SP 800-53 Rev 5 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | AI coding agents fail when delegated access exceeds the approved human task. |
| ASI02 — Tool Misuse | GitHub and MCP tool chains can be abused if tool access is broader than the task. | |
| ASI10 — Rogue Agents | Unbounded agent execution can continue acting beyond the intended delegation window. | |
| Recommendation — Bind each agent run to a human-approved scope and revoke excess privilege immediately. Restrict tool permissions to the minimum actions needed for the current run. Invalidate sessions and remove standing access when the delegated task ends. | ||
| NIST SP 800-53 Rev 5 | IA-5 — Authenticator Management | Delegated execution depends on issuing, limiting, and revoking the credentials used for the run. |
| AC-6 — Least Privilege | The answer centers on task-scoped access and minimum necessary permissions. | |
| AU-2 — Event Logging | Accountability across the delegation chain requires logging approval and action events. | |
| Recommendation — Use short-lived authenticators and revoke them when the delegated task completes. Grant only the permissions required for the specific agent run. Log the human grant, the agent action, and the target system touched. | ||
| NIST Zero Trust (SP 800-207) | AC-6 — Least Privilege | Delegated agents should receive only the access needed for each transaction. |
| Recommendation — Enforce per-request least privilege for agent-mediated actions. | ||
| OWASP API Security Top 10 | API5 — Broken Function Level Authorization | Agent tool calls can become unauthorized functions if the action boundary is weak. |
| Recommendation — Authorize each callable function the agent can invoke. | ||
Practitioner Guidance
What to prioritise: Put the approval point at invocation, not at user enrolment. If the agent is allowed to act in GitHub or through MCP, the first control question should be whether this run has a named human sponsor, a bounded task, and a time-limited grant.
What to verify: Confirm that logs capture the human initiator, the approved scope, the tool or repository touched, and the final action taken. If any of those are missing, you do not have an accountability chain, only activity records.
Decision rule: If a task requires broader or longer-lived access than is acceptable for one run, split it into smaller delegated steps rather than widening the agent’s standing privilege. MCP authorization specification is the right reference point when the tool path itself needs audience-bound, non-passthrough access.
Common mistake: Teams often secure the model interaction and forget the connected systems. The real control objective is not “safe prompting”, it is preventing a well-instrumented agent from becoming a high-speed operator over GitHub, cloud, and internal tooling without fresh approval.
Practitioner takeaway: Govern the agent like a controlled delegation event, not an identity that deserves durable trust; if you cannot prove who authorised the run, what it could do, and when that authority ended, the model is too permissive.