System Prompts as Governance Artifacts in AI Developer Tools: A Forensic Comparative Study

AI developer assistants increasingly behave as tool-mediated agents: they can read files, search repositories, run commands, and sometimes modify working directories. The system prompt that governs such assistants does more than provide task instructions. In practice, it functions as an “invisible constitution” that defines who the assistant is, what it may do, what it must refuse, and when it must stop.

This paper treats system prompts as a distinct governance layer. Rather than evaluating outputs in isolation, we analyze the text-level control structures encoded in system prompts across developer assistants and modes. We ask:

• How do system prompts allocate authority and constrain autonomy in developer assistants?
• What recurring, reusable governance structures appear across tools and modes?
• How do prompt-level controls attempt to mitigate failure modes for tool-using agents?

Contributions:

• A comparative, schema-based analysis of system prompts across IDE and CLI developer assistants and interaction modes.
• A synthesis of recurring prompt-encoded controls into Prompt Governance Primitives (PGPs).
• A risk-oriented mapping from governance primitives to failure classes for tool-mediated agents.

The idea that high-level principles can govern model behavior has a clear antecedent in Constitutional AI, which aligns models via an explicit set of principles used for self-critique and refusal behavior (Anthropic, 2022). While Constitutional AI focuses on training-time alignment and normative principles, this paper examines deployed governance encoded directly in system prompts of developer tools.

Prompt injection and tool-calling vulnerabilities show how tool mediation can be exploited when boundaries are unclear or insufficiently enforced (Wang et al., 2025). Architectural patterns for securing agentic systems emphasize isolation, mediation, and constrained action selection; such patterns motivate treating “tool boundaries” and “action selection rules” as first-class control points (Beurer-Kellner et al., 2025; Masood, 2025).

Industry and practitioner analyses have highlighted that system prompts can be extensive and operationally consequential, functioning as governance documents that specify refusal policies, tool-use rules, and style constraints (Sharma, 2025; Willison, 2025). OpenAI’s discussion of a shift from hard refusals toward “safe-completions” emphasizes how governance can be expressed as output-centric policies and prompt-level constraints rather than binary compliance (OpenAI, 2025). Comparative analyses of model prompts further suggest that system prompts encode different architectural priorities (Forte, 2025).

What prior work less directly examines is the cross-tool, cross-mode governance structure of system prompts in developer assistants specifically—how prompts implement tiered autonomy, action gating, and workspace-integrity safeguards across operational modes (planner, reviewer, executor, full-access agent). This paper addresses that gap via comparative prompt forensics.

3.1 Collection, normalization, and analysis

We treat each assistant’s system prompt(s) as a governance artifact and analyze them using a normalized system-prompt schema. Prompts are collected per assistant and per mode, then normalized into a common representational structure to enable comparison. Each assistant’s modes are treated as constitutional variants within a single governance regime.

3.2 Analytical dimensions

Comparative analysis is performed structurally along invariant dimensions that recur across assistants:

• Authority boundaries: who is the final arbiter (policy, user, model) and how that authority is delegated or constrained by mode.
• Scope and visibility: what the assistant may assume, what state is visible, and what persistence/memory is permitted.
• Tool mediation: which tools exist, how tool use is sequenced, and how side effects are gated.
• Correction and termination: self-checking, stop-and-ask triggers, and hard termination contracts.

3.3 Validity under partial observability

Prompt-level analysis does not guarantee runtime enforcement. However, system prompts are explicit declarations of intended governance and are often the only observable specification of decision rights, tool constraints, and refusal/termination contracts. As such, they are valid for identifying architectural patterns, comparing governance regimes, and extracting reusable control structures—even when implementation details remain opaque.

3.4 Use of AI Assistance

This research was produced with significant AI assistance across the analysis and synthesis pipeline. GPT-5.2 was utilized for primary data analysis, research report generation, and the development of the technical appendix and paper synthesis. ChatGPT was employed for initial idea conception and the iterative refinement of research prompts. ChatGPT Deep Research was used to identify and verify relevant citation sources. Gemini 3 Flash (via GitHub Copilot extension in VS Code) was used for final editorial review and refinement.

4.1 Assistants and modes under study

The analysis covers multiple assistants and modes, including local software engineering agents with execution and review constitutions; terminal assistants with interactive and prompt-oriented variants; CLI assistants with plan-versus-build splits; and IDE assistants with ask/plan/agent tiers and varying sandbox and approval semantics.

4.2 Authority models: partitioned by mode

Authority is consistently partitioned rather than monolithic:

• Policy-supremacy regimes explicitly elevate policy as the final arbiter over user intent.
• User-consent and escalation regimes expand authority conditionally via approval gates or escalation pathways when sandbox constraints block actions.
• Agent-final regimes place final decision authority with the model in “full-access” settings, compensating with stronger stop conditions and non-interference rules.
• Role-narrowing regimes restrict permissible outputs despite tool availability (e.g., review modes that forbid fixes; plan modes that forbid implementation).

Across these regimes, mode boundaries operate as constitutional contracts that reallocate decision rights and permissible side effects.

4.3 Scope and visibility: risk control via bounded context

Prompts frequently treat scope and visibility as governance levers:

• State minimization (statelessness or non-persistence) limits long-horizon autonomy and reduces accumulated implicit commitments.
• Mode-level memory policies treat retention as a first-class risk variable.
• Partial visibility disclosures (e.g., describing sandboxing/network/roots and open tabs without their contents) indicate deliberate control of what the assistant may claim.
• Repository-local overlays (workspace rules) extend governance without changing the core constitution.

4.4 Tool mediation: procedural governance

Tooling is the dominant enforcement surface. Prompts constrain tool invocation via procedural obligations:

• Side effects are routed through declared tools (shell, file edits, search, tasks, web/GitHub access).
• Modes implement side-effect gating (read-only plan/ask; write/execute agent).
• Some regimes mandate tool sequencing (e.g., staged planning workflows) and encode efficiency or legibility constraints via parallelization and specialized-tool preference.
• Capability and permission are decoupled: tools may exist while specific outcomes are forbidden.

4.5 Correction and termination

Correction loops and termination logic act as backstops:

• Self-checking behaviors appear across assistants, shifting from “does it work?” in executor modes to “is it compliant?” in reviewer/planner modes.
• Workspace-integrity protection is encoded as explicit stop-and-ask triggers when unexpected changes are detected.
• Some modes terminate via strict output contracts (e.g., schema-conforming responses).

5.1 Definition and rationale

We define a Prompt Governance Primitive (PGP) as a recurring, prompt-encoded control structure that allocates authority, bounds scope and visibility, mediates tool use, constrains outputs, and/or defines correction and termination behavior.

5.1.1 PGP Taxonomy Diagram

PGPs qualify as “primitives” because they recur across assistants, compose into larger governance regimes, and can be referenced independently of any single prompt.

System prompts encode mitigations intended to address concrete operational risks for tool-mediated agents.

• Autonomy drift: mitigated via mode partitioning (plan vs build), approval gates, and explicit stop conditions.
• Workspace corruption and unintended side effects: mitigated via conservative change doctrines, prohibitions on destructive git actions, read-before-edit rules, and stop-on-unexpected-change circuit breakers.
• Instruction leakage: mitigated via explicit confidentiality constraints that treat system instructions as non-disclosable.
• Tool abuse and prompt injection: mitigated indirectly via tool mediation and procedural constraints. This aligns with evidence that adversarial injection can manipulate tool-calling if mediation boundaries are weak or conflated with untrusted inputs (Wang et al., 2025).
• Hallucination and ungrounded claims: mitigated by requiring tool-grounded inspection and, in some regimes, consulting authoritative documentation tools for capability questions.

These prompt-encoded controls should be understood as intended mitigations rather than guarantees; their effectiveness depends on runtime enforcement.

The comparative analysis suggests that system prompts already function as constitutions for developer assistants. This has implications for both engineering practice and governance:

• Agent-first development: governance should be treated as an explicit architecture layer, not incidental prompt text. Mode boundaries can be used to allocate autonomy tiers and constrain side effects.
• Multi-agent systems: specialized roles (planner, reviewer, executor) can be separated into distinct constitutions with explicit capability-permission boundaries.
• Enterprise governance: prompt registries, audits, and versioning are natural complements to treating prompts as governance artifacts (VerityAI, 2025 as cited).
• Applied AI safety engineering: prompt-level primitives provide a composable vocabulary for designing tool-mediated agents that must operate conservatively in real repositories.

Prompt-level analysis cannot establish runtime enforcement fidelity. Some regimes reference external policy documents or repository-local governance layers whose content is not fully observable, and several controls are specified as rules without exposing detection mechanisms.

Across IDE and CLI developer assistants, system prompts operate as constitutional governance documents. Abstracting these structures into Prompt Governance Primitives (PGPs) provides a reusable vocabulary for building, auditing, and comparing tool-mediated agents.