Agent-Driven Dependency Updates

• Agent-driven dependency updates are defined as autonomous or semi-autonomous AI systems that programmatically manage changes to a project’s third-party dependency sets using LLMs, negotiation engines, or multi-agent RL.
• Architectural patterns include LLM-agent orchestration with retrieval-augmented generation, dynamic multi-agent RL planning, and self-adaptive feedback loops that address error correction and risk management.
• Empirical analyses indicate that while agent-driven updates may increase vulnerability introduction rates, they enhance library diversity and automation efficiency, prompting the need for integrated safety guardrails.

Agent-driven dependency updates refer to the application of autonomous or semi-autonomous software agents—especially those based on LLMs, specialized negotiation engines, or multi-agent reinforcement learning (MARL) systems—to reason about, plan, execute, and monitor changes to a software project’s third-party dependency sets. Such systems operate across contexts ranging from open-source maintenance (e.g., via pull requests) to dynamic multi-agent control environments and agentic deployment pipelines. Contemporary research covers the architectural patterns, empirical security and maintenance impacts, algorithmic formulations, and user-facing challenges at scale. This article provides a comprehensive, technically rigorous synthesis of foundational models, empirical findings, agentic workflows, and salient risks in agent-driven dependency updating.

1. Formal Definitions and Scope

Agent-driven dependency updates are characterized by several core elements across software engineering and interactive learning domains.

• Agent-driven dependency update: Any addition, removal, or version change to a project dependency (tuple ⟨package, version⟩ in a manifest file, e.g. package.json, requirements.txt) performed programmatically by an AI coding agent—examples include Copilot, Devin, OpenAI Codex, Cursor, or Claude Code (Singla et al., 1 Jan 2026).
• Dependency: Direct package declarations in recognized manifest files; analysis may extend to the entire dependency graph (direct and transitive dependencies) (Alhanahnah et al., 2024).
• Update events: Include import/addition, removal, or explicit version lock/bump for libraries, as well as structural or semantic code edits necessary for compatibility after an update (Tawosi et al., 3 Oct 2025).
• Agent types:
  • LLM-based PR authors and upgrade bots (e.g., LADU, Dependabot, Renovate) (Tawosi et al., 3 Oct 2025, He et al., 2022).
  • Negotiation and mediation agents to reduce alert fatigue (Kula, 10 Feb 2025).
  • Multi-agent RL planners adapting to dependency dynamics (e.g., traffic signal control, adversarial interactive environments) (Zhang et al., 23 Feb 2025, Mirzaeedodangeh et al., 13 Nov 2025, Li et al., 2022).
  • Autonomous deployment agents with case-based debugging (Chen et al., 31 Mar 2025).
• Multi-agent reinforcement learning (MARL) context: Dependencies reflect either physical couplings (e.g., action and observation overlap, reward coupling) or dynamic interaction topologies (Zhang et al., 23 Feb 2025, Li et al., 2022).

These definitions provide the basis for quantifying, analyzing, and designing agent-driven systems spanning codebase maintenance, automated deployment, and distributed control.

2. Architectural Patterns and System Design

Agent-driven dependency management architecture varies by operational context but follows several recurring paradigms:

A. LLM-agent Orchestration for Codebase Upgrades and Dependency Edits

• Component agents: LADU partitions functionality into Summary Agent (code and method summarization), Control Agent (change localization, migration-guide parsing), and Code Agent (concrete code rewriting) (Tawosi et al., 3 Oct 2025). Orchestration proceeds iteratively: summary extraction → relevant code selection → instruction generation → code patching → test/compile → loop or human handover.
• Retrieval-Augmented Generation (RAG): DepsRAG exemplifies RAG applied to dependency knowledge graphs. A single LLM orchestrates KG queries (via CypherQueryTool, GraphSchemaTool for schema) and web-facing fallback retrievals in response to user queries, augmenting LLM prompts with retrieved facts before answer synthesis (Alhanahnah et al., 2024). No similarity scoring over embeddings; agentic logic is embedded in tool orchestration rather than explicit sub-agent negotiation.
• Feedback and self-adaptation: AI2Agent employs guideline-driven execution pipelined with self-adaptive debug (error-driven correction search, case-based retrieval, and environment-proven fixes) and case-based solution accumulation to steadily improve over time (Chen et al., 31 Mar 2025).

B. Multi-Agent RL and Dynamic Parameter Updates

• Dependency-aware parameter update: DQN-DPUS dynamically switches between diagonal-only versus full-matrix Q-network weight updates conditional on detected spill-back, i.e., agent dependency between traffic intersections. Independent RL applies when sub-environments are decoupled (Zhang et al., 23 Feb 2025).
• Bidirectional dependency modeling in MARL: ACE unrolls simultaneous agent decisions into a sequential-chain single-agent MDP, with forward (past action) and backward (subsequent agent) dependencies encoded in the value backup and network structure (Li et al., 2022).
• Safety guarantees in agent-environment feedback: Iterative conformal prediction maintains probabilistic safety certificates across policy updates by quantifying policy-to-trajectory sensitivity and inflating prediction “tubes” to account for adversarial distribution shift (Mirzaeedodangeh et al., 13 Nov 2025).

Agent-driven frameworks are thus defined by their granular decomposition of reasoning/planning roles, degree of centralization, and capacity for internal feedback and adaptation.

3. Empirical Impacts: Security, Maintenance, and Ecosystem Effects

The large-scale quantitative effects of agent-driven dependency updates have now been directly measured:

A. Security Posture

• Vulnerability introduction rates: Agents select known-vulnerable versions at a higher rate (2.46%) than humans (1.64%) when performing additions and updates as measured on 117,062 dependency changes across 2,807 repositories (Singla et al., 1 Jan 2026).
• Remediation severity: Agent-introduced vulnerable dependencies require a major-version upgrade for remediation in 36.8% of cases, compared to 12.9% for human edits.
• Aggregate effects: Agents produce a net vulnerability increase (+98), whereas human edits yield a reduction (–1,316) after considering both introduced and fixed vulnerabilities.
• Cause analysis: LLM agents usually lack runtime vulnerability screening, default to the latest available version, and spread dependency edits over numerous narrowly scoped PRs, compounding risk introduction (Singla et al., 1 Jan 2026).

B. Library Usage and Dependency Practices

• Import vs. dependency addition frequency: In agent-authored PRs, 29.5% import at least one library, but only 1.3% add a new dependency to the manifest (Twist, 12 Dec 2025).
• Version pinning: 75% of newly declared agent-managed dependencies specify a version, offering a margin of reproducibility over non-agentic LLM completions.
• Functional roles: Most new dependencies added by agents are for testing/tooling (e.g., pytest, xUnit) rather than core production paths.
• Diversity: Agents import a far larger set of unique libraries (3,988 across 7,888 PRs) than prompt-completion LLMs, suggesting enhanced ecosystem coverage, but possibly raising human review costs (Twist, 12 Dec 2025).

C. User and Developer Experience

• Notification fatigue: 31.1% of surveyed developers report Dependabot opens more PRs than they can handle, leading to config changes post-adoption to suppress update volume (e.g., adjusting scan cadence, PR limits, or ignore-lists) (He et al., 2022).
• Update suspicion and compatibility: Scarcity of compatibility-score evidence reduces trust—only 3.4% of PRs had computable compatibility scores, and higher scores correlate positively but weakly with PR merges (He et al., 2022).
• Abandonment: 11.3% of projects deprecate or abandon agent-based bots (e.g., Dependabot) in favor of alternatives, with notification fatigue and lack of grouped updates as top reasons.

4. Algorithms, Planning, and Reasoning Mechanisms

Agent-driven dependency update systems embody a range of agentic reasoning and adaptation mechanisms:

A. LLM Agent Workflows:

• LADU/Tawosi et al.: Multi-agent planning involves input preprocessing (summarization to ∼20% token budget), migration guide parsing (LLM-extracted rewrite rules), context selection via summary matching, and code patch application, with feedback from test/compile phases (Tawosi et al., 3 Oct 2025). Code diffs are generated under an explicit instruction plan; system reverts to human-in-the-loop if progress stalls.
• DepsRAG: Single-LLM tool orchestrator invoking KG and web retrieval modules per user query; no update or planning loop, and no distinct Generator- or Critic-Agent in the current design (Alhanahnah et al., 2024).
• AI2Agent: Algorithm 1 specifies iterative guideline-driven execution, self-adaptive debug (error-driven fix search via past-case retrieval and LLM heuristics), and solution accumulation (Chen et al., 31 Mar 2025).

B. Multi-Agent Reinforcement Learning:

• DQN-DPUS: Switches between independent and joint (dynamic) Q-network parameter updates based on a binary spill-back indicator; no spill-back allows per-agent diagonal updates, dependencies trigger full centralized updates (Zhang et al., 23 Feb 2025). Empirically, this reduces convergence time and stabilizes learning in dynamic coupling regimes.
• ACE: Implements sequential decision-making over “SE-states,” constructing a forward-backward dependency chain. The SE-MDP reduction to single-agent form eliminates non-stationarity, with practical and theoretical scalability to larger agent teams (Li et al., 2022).
• Conformal Prediction Adjustments: Each policy update in the agent changes the environment’s distribution (circular dependency); adversarially robust CP inflates safety sets by a quantifiable sensitivity bound βT ∥π{j+1}–πj∥∞, ensuring valid confidence even as the policy/environment evolves jointly (Mirzaeedodangeh et al., 13 Nov 2025).

5. Challenges, Limitations, and Recommendations

Key limitations and open challenges for agent-driven dependency update systems include:

A. Security, Trust, and Guardrails

• Real-time vulnerability checks must be integrated at PR time. Automatic screens against advisories (e.g., ecosyste.ms) can block merges of known-vulnerable versions and suggest immediate safe alternatives considering semver or compatibility constraints (Singla et al., 1 Jan 2026).
• Registry-aware guardrails propose not only scanning selected versions but querying for the highest available safe versions within requested constraint windows and providing guidance or automated manifest patches.

B. Human Factors: Alert Fatigue, Transparency, and Explainability

• Negotiation agents—as conceptualized for Dependabot—prioritize update alerts using utility functions weighted by security, compatibility, ecosystem uptake, and maintainer workload under a global fatigue budget, with transparency via explainer dialogs, provenance tracing, and maintainability feedback loops (Kula, 10 Feb 2025).
• Configurability, autonomy, transparency, and self-adaptability are core for maintainers: fine-grained control over update frequency and grouping, autonomous (safe) merges, clear evidence-based risk communication, and adaptive notification/policy tuning (He et al., 2022).

C. Scope of Reasoning and Planning

• DepsRAG, as published, supports only analysis, not planning or execution of updates: no multi-agent transaction protocols, scoring functions, or empirical measures of update success currently available.
• Migration guides and policy-planning agents facilitate only syntactic rewrites and rename detection, not deep behavioral refactorings or semantic code adaptation in the absence of detailed human-authored documentation (Tawosi et al., 3 Oct 2025).

D. Evaluation Gaps

• Most current evaluations focus on coverage, precision, or time/efficiency metrics. Direct measures of “update correctness,” “conflict resolution rates,” or fully context-aware planning success in heterogeneous codebases remain limited.
• Agent systems learn over time; empirical studies to date amalgamate structure and learning curves but seldom isolate the benefits of long-horizon case accumulation or active adaptation (Chen et al., 31 Mar 2025).

6. Implications and Future Directions

Agent-driven dependency management, whether pursued via LLMs, negotiation engines, MARL frameworks, or case-based deployment pipelines, is redefining both the technical substrate of dependency reasoning and the human-agent boundary in maintenance workflows.

• Future systems must integrate continuous, real-time advisory and registry validation; enable risk-aware, explainable negotiation; and maintain fine configurability alongside adaptive, feedback-driven noise suppression.
• Multimodal agent orchestration—combining code retrieval, graph-based reasoning, and probabilistic safety guarantees—presents avenues for both more robust automation and deeper human trust in agentic interventions.
• Empirical evidence underscores the need for proactive guardrails: absent runtime advisory screening, agent PRs increase net vulnerabilities, particularly when update choices are made without centralized policy coordination (Singla et al., 1 Jan 2026).
• The general principle of dynamically adapting the degree of agent centralization as dependencies coalesce or dissipate in time (“anytime” MARL, dynamic parameter updates) is expected to generalize across distributed control and collaborative software engineering domains (Zhang et al., 23 Feb 2025, Li et al., 2022).
• Integration of agent-driven updating with project-specific, crowd-sourced, and ecosystem-aware context should enhance both robustness and scalability; detailed quantitative, user-experience, and adversarial robustness evaluations remain urgent open topics.

Agent-driven dependency updates thus stand at the confluence of automation, security, distributed coordination, and human-centric software maintenance, with rigorous empirical and algorithmic work now delineating both their promise and critical technical pitfalls.