Treat the agent as a scoped operational actor, not as a generic automation layer. Define read, scaffold, and write permissions separately, review which resource classes it can touch, and ensure its authority stops at the smallest set of Konnect objects needed for the task.
What delegated access means for AI coding agents
delegated access for AI coding agents should be treated as an explicit authority model, not a convenience feature. The agent needs a defined role, a bounded task scope, and a clear ceiling on what it may read, create, modify, or delete. That framing matters because coding agents often operate across IDEs, terminals, repositories, and connected services, which makes ambiguous authority easy to overextend.
The practical unit of control is the resource class, not the “agent” as a generic label. A team should decide whether the agent may inspect source code, scaffold files, open pull requests, run commands, or write to production-adjacent systems, then separate those permissions instead of bundling them into one broad approval.
Delegation also needs a lifecycle. Access granted for a single task should not become standing access for all future tasks, and authority should be tied to the smallest effective scope. For AI coding work, that usually means narrow repository scope, tightly constrained environment access, and explicit separation between low-risk read activity and high-risk write activity.
Where governance breaks down in practice
Teams usually get into trouble when they confuse “productivity delegation” with “unbounded operational authority.” Once an agent can call tools, interpret repository content, and act on tokens or workspace credentials, the issue is no longer just code generation. It becomes control of what the agent can reach, what assumptions it can inherit, and what downstream systems it can influence.
Good governance starts with classifying the objects the agent may touch. For a platform tool, that may include project configuration, build definitions, secrets handling, deployment triggers, or infrastructure references. Each object class should have an explicit policy decision, because allowing write access to one class often creates indirect authority over others.
Teams should also decide how much autonomy is acceptable when the agent moves from suggestion to execution. A scaffold-only agent can be governed differently from a write-capable agent, and a write-capable agent must be evaluated for blast radius, rollback, and approval boundaries before it is trusted with anything persistent.
Anchor the operating model in a least-privilege path and review it through an identity lens. AI Agent Authorisation Guide is useful here because it frames task-scoped access, per-action decisions, and approval gates as the normal baseline rather than the exception.
How to set the boundary for read, scaffold, and write authority
The cleanest model is to separate read, scaffold, and write authority into distinct permission bands. Read access should cover only the repositories, documents, or APIs required for context. Scaffold access should allow the agent to create drafts, branches, or temporary artifacts without the ability to publish them broadly. Write access should be reserved for the narrowest set of objects that genuinely need durable change.
That separation is especially important when the platform tool exposes multiple resource classes through one interface. If an agent can read a configuration object, it does not automatically need the right to edit it. If it can scaffold code, it does not automatically need deployment authority. If it can open a merge request, it does not automatically need the ability to merge, release, or rotate credentials.
Teams should also verify whether the authority is delegated directly to the agent or inherited through a human’s credentials. Delegation through user credentials can blur accountability, weaken revocation, and make it harder to tell whether a human or agent initiated a risky action. A clearer design gives the agent its own bounded identity path and keeps sensitive actions attributable.
For teams building the access model from first principles, Agentic AI Identity Guide provides the right lens for identity, delegation, registration, and retirement, while RFC 8693: OAuth 2.0 Token Exchange is the clearest standard for on-behalf-of and delegated token flows.
Risk and Threat Considerations
Delegated access becomes dangerous when a coding agent is allowed to act with more authority than the task actually requires. Overbroad tokens, shared credentials, or permissive workspace trust can turn a harmless helper into a write-capable actor that can alter code, touch infrastructure, or expose secrets at machine speed.
Failure mechanism: The agent inherits excessive authority, then a prompt, repository artifact, or malicious tool output pushes it into executing an unsafe action that is still valid under its permissions.
Impact: The result can be unauthorized changes, secret exposure, destructive command execution, or lateral impact into connected systems that were never intended to be part of the task.
For a concrete example of how excess authority changes outcomes, Amazon Q Developer extension compromise 2025 shows how an over-scoped CI token can convert a prompt into destructive downstream action, and Replit AI agent database deletion 2025 shows how write authority against live systems can produce immediate production damage.
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 addresses the attack surface, NIST SP 800-53 Rev 5 sets the technical controls, and ISO/IEC 27001:2022 defines the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Directly addresses delegated authority and over-scoped agent permissions. |
| ASI02 — Tool Misuse | Platform tools are the mechanism through which delegated agent authority is exercised. | |
| Recommendation — Bound agent permissions to task-scoped authority and require approval for sensitive actions. Constrain tool permissions so the agent can invoke only the actions needed for the task. | ||
| NIST SP 800-53 Rev 5 | AC-6 — Least Privilege | The question is about minimizing delegated access for an operational actor. |
| IA-9 — Service Identification and Authentication | AI coding agents operate as non-human actors needing controlled authentication paths. | |
| IA-5 — Authenticator Management | Delegated access depends on controlling tokens, keys, and other credential material. | |
| Recommendation — Restrict the agent to the minimum permissions required for each task. Authenticate the agent with a distinct service identity and tightly managed credentials. Rotate and scope the agent's credentials so delegated access expires cleanly. | ||
| ISO/IEC 27001:2022 | A.5.15 — Access control | Delegated access governance depends on defined access rules and restriction of authority. |
| A.8.2 — Privileged access rights | Write-capable agents may function as privileged actors over platform tools. | |
| Recommendation — Define and enforce role-based access rules for each agent task and resource class. Treat agent write access as privileged access and review it separately from read access. | ||
Practitioner Guidance
What to verify: Confirm that the agent’s permissions are mapped to concrete object classes, not to a vague platform role. If you cannot name the exact repositories, environments, APIs, or deployment actions it may touch, the delegation boundary is not mature enough for production use.
Decision rule: If the task only requires inspection or draft generation, keep the agent in read or scaffold mode. If it needs write access, require an explicit approval path and treat the permission as temporary, narrowly scoped, and easy to revoke.
Common mistake: Teams often approve the agent once, then let that approval drift into a broad standing entitlement. That is the point where delegated access stops being an operational aid and starts becoming a privilege sprawl problem.
Practitioner takeaway: The right control is not “allow AI to help,” it is “limit exactly what the agent can change, for how long, and under whose authority.”
Related resources from NHI Mgmt Group
- How should security teams govern API keys used for generative AI access?
- How should security teams govern coding agents that already have access to production tools?
- How should security teams govern AI agents and model access after an acquisition expands the security platform?
- How should security teams centralise access to coding agents without forcing developers into shadow AI tools?