DecisionOps Runtime: Governing Architectural Decisions in Agent-Driven Software Development

Author: S. R.
Date: February 2026

Abstract

Agentic coding systems have increased the throughput of software change by enabling autonomous, multi-step modifications across large codebases. However, enterprises report that higher change velocity does not reliably translate to improved delivery outcomes. Recent empirical evidence suggests that AI assistance often behaves as an amplifier of pre-existing organizational strengths and weaknesses, rather than a universal productivity accelerator [1]. Moreover, developer-perceived productivity gains can diverge substantially from measured outcomes, indicating the need for objective, system-level instrumentation and controls [2].

We formalize this phenomenon as the Acceleration Problem: a throughput imbalance in which machine-generated change exceeds an organization's capacity to govern architectural intent, compliance, and integration safety. We propose DecisionOps Runtime, a governance architecture that (i) represents architectural decisions as a machine-readable Decision Graph, (ii) injects decision context into agents via a Decision Context API compatible with tool-mediated agent workflows, (iii) enforces decision compliance at pull-request time through a deterministic PR Gatekeeper, and (iv) measures decision adherence and drift using Decision Observability. We introduce measurable constructs (Decision Coverage Rate and a Decision Drift Index) and propose an evaluation methodology that can be replicated in GitHub-first enterprise settings. The goal is to preserve agentic velocity while reducing architectural drift, review overload, and integration regressions.

1. Introduction

AI-assisted software delivery has shifted from inline completion toward agentic systems that plan and execute multi-step changes. This transition increases the rate and parallelism of code changes and shifts the delivery bottleneck from writing code to governing change. DX reports high adoption levels and finds that daily AI users ship substantially more pull requests than non-users, while noting mixed impacts on quality [3]. DORA's 2025 research similarly argues that AI primarily amplifies organizational systems: capable organizations see larger gains, while brittle ones see amplified dysfunction [1].

This paper addresses a gap: architectural governance practices have historically assumed human-speed throughput (human authors, human reviewers), while agentic tooling can generate change at machine speed. As a result, teams observe repeated PR-level friction:

• Does this violate prior architectural decisions?
• Is this consistent with platform guardrails?
• Do we need a new decision?

These checks are cognitively expensive and often performed late, after design debt has already accumulated.

1.1 Research Questions

We focus on four research questions (RQs):

• RQ1: What failure mode emerges when agentic code throughput exceeds the throughput of architectural governance?
• RQ2: Can architectural decisions be represented in a machine-usable form that supports contextual injection into agent workflows?
• RQ3: Can PR-time enforcement reduce the merge of decision-violating changes without materially reducing developer flow?
• RQ4: Can decision adherence and drift be measured reliably over time beyond anecdotal review?

1.2 Contributions

This paper contributes:

1. A formalization of the Acceleration Problem as a throughput imbalance between change generation and governance capacity.
2. The DecisionOps Runtime architecture: Decision Graph, Decision Context API, PR Gatekeeper, Agent Hooks, and Decision Observability.
3. Two primary measurable constructs for adoption and control: Decision Coverage Rate and a Decision Drift Index.
4. A GitHub-first evaluation plan suitable for enterprise pilots.

2. Background and Related Work

2.1 ADRs and Decision Documentation

Architectural Decision Records (ADRs) are a widely adopted practice for documenting architectural rationale. However, in typical implementations, ADRs are narrative artifacts (Markdown and wikis) that are difficult to operationalize as constraints or policies at implementation time. This creates a disconnect between recorded intent and enforcement.

2.2 Socio-Technical Coordination Under Increased Throughput

Software architecture is shaped by organizational communication structure (Conway's Law). Increased throughput raises coordination load and magnifies latent coupling and governance weaknesses. This is consistent with the AI-as-amplifier framing in DORA's 2025 report, which finds that organizational systems strongly determine whether AI adoption improves outcomes or exacerbates dysfunction [1].

2.3 Empirical Signals: Output vs. Outcomes

DX's Q4 2025 AI-assisted engineering report finds high adoption and increased PR throughput among daily AI users, while emphasizing the need for quality-oriented measures (maintainability, revert rates, reliability) [3]. Meanwhile, METR's controlled study observed that experienced developers were slower with AI assistance in specific settings, even while believing they were faster, highlighting an observability and measurement gap [2]. These findings motivate governance and instrumentation that distinguish more code from better delivery.

2.4 Policy-as-Code and Deterministic Gates

Policy-as-code approaches (for example, OPA-style rule evaluation) demonstrate that deterministic checks can be embedded into CI/CD pipelines to enforce constraints consistently. DecisionOps Runtime extends this idea from infrastructure and compliance policy to architectural intent and decision lifecycle governance.

3. The Acceleration Problem: A Formal Model

We define the Acceleration Problem as a mismatch between:

• Generation throughput: the rate at which code changes are produced by humans and agents.
• Governance throughput: the rate at which changes can be validated against architectural intent, compliance, and integration safety.

3.1 Throughput Definitions

Let:

• T_g: change generation throughput (for example, PRs/day, changed files/day, diff size/day)
• T_v: governance throughput (for example, reviewed PRs/day at required depth, validated decisions/day)

Define the Acceleration Coefficient:

AC = T_g / T_v

When AC >> 1, governance becomes the binding constraint; drift and instability increase because decisions are not enforced consistently.

3.2 Why ADRs Fail Under High AC

Narrative ADRs do not scale under high AC because:

• Retrieval is expensive (humans searching documents).
• Mapping ADRs to impacted files or services is manual.
• Enforcement is mostly social (review comments), not deterministic.

Thus, high change velocity yields architectural entropy: local optimizations that pass unit tests but violate cross-cutting decisions (data residency, dependency constraints, platform standards).

4. DecisionOps Runtime

DecisionOps Runtime is a governance architecture designed to reduce AC by increasing T_v through automation and by shifting validation earlier (task time instead of review time).

4.1 System Overview

Five pillars:

1. Decision Graph: machine-usable decision store with lifecycle and relationships.
2. Decision Context API: agent-consumable tools to retrieve and check decisions.
3. PR Gatekeeper: deterministic enforcement and coverage checks at PR time.
4. Agent Hooks: tool-specific instructions to ensure decisions are consulted.
5. Decision Observability: event capture, metrics, and drift monitoring.

4.2 Design Goals

1. Reduce contradictory architectural changes.
2. Increase measurable decision coverage.
3. Shorten review cycles caused by decision ambiguity.
4. Make decision context portable across agent tools.

4.3 Non-Goals

• Building a full coding agent.
• Replacing issue tracking systems.
• Supporting all VCS platforms in v1 (GitHub-first).

5. Decision Graph

5.1 Conceptual Model

The Decision Graph models decisions as first-class entities with lifecycle states:

• Proposed
• Accepted
• Deprecated
• Superseded

Relationships include:

• supersedes
• conflicts_with
• depends_on
• applies_to

5.2 Example Schema

{
  "id": "DEC-2026-0042",
  "title": "Use PostgreSQL for transactional workloads",
  "status": "accepted",
  "constraints": [
    "No MySQL for new transactional services"
  ],
  "scope": {
    "repos": ["org/payments-api"],
    "paths": ["services/payments/**"]
  }
}

6. Decision Context API

Core operations:

• resolve_for_diff(diff, repo)
• check_conflicts(change_plan, repo)
• search(query)
• create_draft(context)
• link_pr(pr_url, decision_ids)

Conflict evaluation combines deterministic rule matching with contextual scoring.

7. PR Gatekeeper

The initial enforcement mechanism integrates as a GitHub App.

Check types:

1. Decision Reference Check
2. Conflict Check
3. Coverage Check
4. Lifecycle Check

Policy modes:

• Advisory
• Soft-block
• Hard-block

Overrides require an authorized approver role and an audit trail entry.

8. Agent Hooks

Agents must consume architectural context before implementation.

Instruction bundles enforce:

• Pre-implementation decision resolution.
• PR description compliance summaries.
• Explicit declaration of decisions considered.

9. Decision Observability

Governance metrics:

10. Security Model

• Tenant isolation per organization.
• Encryption in transit and at rest.
• Signed webhook validation.
• Immutable audit log.
• No code used for model training by default.

11. Evaluation Plan

Pilot hypotheses:

• >=70% decision coverage on pilot repositories.
• >=30% reduction in review rounds caused by architectural clarification.
• <=5% false-positive conflict rate after tuning.

12. Conclusion

As coding agents increase development throughput, architectural governance must evolve from documentation to runtime enforcement.

DecisionOps Runtime provides a structured framework to:

• Model decisions as machine-usable artifacts.
• Inject context at task time.
• Enforce compliance at PR time.
• Measure architectural drift longitudinally.

It represents a foundational step toward scalable governance in agent-driven software systems.

References

1. DORA research, 2025: AI as an organizational amplifier.
2. METR study: perceived vs measured AI productivity outcomes.
3. DX Q4 2025 AI-assisted engineering report.