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Reality Is the Final Verifier: On Two Key Gaps in Agentic Software Engineering

Alexander Krentsel, Shubham Agarwal, Mert Cemri, Shu Liu, Sidharth Sankhe, Ziming Mao, Matei Zaharia, Ion Stoica

arXiv:2609.12039v1cs.SEcs.AI

TL;DR

Formal verification against requirements and an environment model cannot by itself guarantee acceptable deployed behavior because each may diverge from its real-world target. The paper uses a two-gap framework to explain reward hacking and hallucination, then proposes an assurance-revision loop; its central conclusion is that open-world gaps generally cannot be certified closed, so assurance must be maintained by narrowing them.

  • Problem

    Requirements and environment models only approximate stakeholder intent and the real world, so passing evaluation or formal verification may not ensure acceptable deployed behavior.

    Source: §9 · p. 14

  • Method

    The paper develops a two-gap framework and proposes an outer assurance-revision loop that uses stakeholder judgment and empirical evidence to revise requirements, models, or evaluators.

    Source: §9 · pp. 13–14

  • Results

    The framework explains reward hacking as exploiting omissions and hallucination as widening gaps through fabrication, while neither gap can generally be certified closed in an open, changing world.

    Source: §9 · p. 14

  • Takeaways & Limitations

    Assurance requires ongoing stakeholder judgment and empirical evidence, with accountable human judgment and faithful, often costly evaluation as principal bottlenecks.

    Source: §9 · p. 14

    Requirement closure is constrained by tacit stakeholder intent and the infeasibility of assessing every relevant execution in the real world.

    Source: §3.1 · p. 5

Abstract

from arXiv · show

Software development follows an implementation-verification loop in which developers or agents iteratively revise an implementation until an evaluator, such as a test suite, accepts it. The evaluator checks the implementation against a set of requirements under a model of the deployment environment. Yet even a formal proof that the implementation satisfies the requirements under the model cannot guarantee acceptable behavior after deployment. Requirements only approximate stakeholder intent, and the model only approximates the real deployment environment. We call these together - requirement gap and model gap - the two-gap framework, which unifies the main failure modes of agentic software engineer-ing: reward hacking exploits omissions in the requirements or model, while hallucination widens the gaps by fabricating requirements or environment assumptions. Because neither gap can generally be certified closed in an open, changing world, the goal shifts from closing them to continuously narrowing them. We therefore propose an assurance-revision loop that uses deployment evidence to revise the requirements, model, or evaluator when stakeholders reject the resulting behavior. We then cast assured agentic development as a resource-allocation problem over human judgment, agent capability, and compute. The two principal bottlenecks mirror the two gaps: human judgment for the requirement gap and faithful, costly evaluation for the model gap. Reality remains the final verifier: acceptable behavior under actual deployment conditions is the ultimate test, while predeployment evaluations remain proxies for it.

1 INTRODUCTION

The paper frames failures in agentic software engineering as gaps between stakeholder intent and requirements, and between the real world and its model. It proposes narrowing those gaps through assurance revision, with human judgment and faithful evaluation as key bottlenecks.

1 INTRODUCTION

  • The two-gap framework distinguishes the requirement gap between stakeholder intent and requirements from the model gap between the real world and its environment model.A proof of correctness under the model cannot guarantee that deployed behavior meets stakeholder intent.Source: §1 · pp. 1–2
  • Reward hacking exploits omissions in requirements or models, while hallucination widens the gaps by fabricating requirements or environmental assumptions.In the key-value example, predictable benchmark values and the missing persistence requirement allow an implementation to pass while failing stakeholder intent.Source: §1 · pp. 1–2
  • Agents magnify these risks through rapid optimization, limited access to local context, fabricated assumptions, and automated deployment at scale.Adding reviewers that share the same incomplete artifacts cannot eliminate the risk.Source: §1 · p. 2
  • An outer assurance-revision loop uses stakeholder judgment and deployment evidence to revise requirements, models, or evaluators when behavior is rejected.The inner implementation-verification loop then runs again.Source: §1 · p. 2
  • Assurance is framed as allocating human judgment, agent capability, and compute, with accountable human judgment and faithful, costly evaluation as principal bottlenecks.Humans retain authority over high-stakes judgments and remain in the outer loop for the foreseeable future.Source: §1 · p. 2

IMPLEMENTATION-VERIFICATION LOOP AND ITS LIMITS

The inner loop revises an implementation against fixed requirements, an environment model, and an evaluator; its acceptance does not establish that the requirements capture intent or the model represents reality. The section distinguishes those external gaps from an evaluation gap within the loop that can in principle be closed.

2.1 The inner loop with fixed 𝑅, 𝑀, and 𝐸

  • The implementation-verification loop iterates on an implementation until an evaluator accepts it against requirements and an environment model.Requirements aim to capture stakeholder intent, while the model represents relevant deployment conditions; both can diverge from their targets.Source: §2.1 · pp. 2–3

2.2 The requirement gap

  • A requirement gap separates stakeholder intent from stated requirements, which may omit, ambiguously express, conflict over, or fail to track changing expectations.The key-value example lacks an explicit requirement to store client-supplied values.Source: §2.1 · p. 3, §2.2 · p. 3

2.3 The model gap

  • A model gap arises when the environment model omits or misrepresents conditions that affect whether evaluator acceptance predicts acceptable deployment behavior.Execution and evaluator assumptions can both matter, and models may age as software, dependencies, hardware, and procedures change.Source: §2.3 · pp. 3–4

2.4 The evaluation gap: an internal, closable gap

  • The evaluation gap occurs when the evaluator accepts an implementation that violates requirements on an execution admitted by the model; a sound proof can close it for fixed requirements and model.This does not establish that the requirements capture intent or the model represents the real world.Source: §2.4 · pp. 4–5
  • The three gaps affect different assurance claims: the evaluation gap weakens evidence of compliance within the loop, while the requirement and model gaps weaken its connection to deployment assurance.Closing the evaluation gap strengthens verification but cannot overcome the two external gaps; narrowing those gaps strengthens assurance.Source: §2.4 · pp. 4–5

3 THE TWO GAPS IN PRACTICE

For open, changing systems, available evidence can narrow the requirement and model gaps but generally cannot certify them closed. The section explains why stakeholder intent and real-world conditions resist complete specification and verification.

3 THE TWO GAPS IN PRACTICE

  • Recorded requirements, models, stakeholder judgments, evaluator results, and deployment observations provide only an incomplete projection of intent and reality.They can narrow the gaps, but generally cannot prove them closed in open and changing systems.Source: §3 · p. 5

3.1 Why the requirement gap is fundamentally hard to close

  • The requirement gap resists closure because intent is partly tacit, stakeholders refine it through interaction, and judging every relevant execution is generally infeasible.A learned surrogate may match observed judgments yet diverge on unseen cases; a formal surrogate would need to capture tacit knowledge and changing views.Source: §3.1 · p. 5

3.2 Why the model gap is fundamentally hard to close

  • The model gap cannot generally be certified closed because direct real-world testing is costly or risky, unknown conditions may arise later, and formalization cannot establish that the model includes every relevant condition.Even when both intent and reality are bounded, closure applies only to the specified property; extending scope introduces further potential gaps.Source: §3.2 · pp. 5–6

4 AI AGENTS MAGNIFY THE GAPS

AI agents magnify longstanding requirement and model gaps through systematic search, fabrication, and rapid deployment. More automated review can catch defects but cannot replace stakeholder judgment or real-world evidence.

4 AI AGENTS MAGNIFY THE GAPS

  • Agents turn longstanding gaps into greater risks through systematic exploitation, hallucination, and rapid propagation of failures at deployment scale.The paper notes that the gaps themselves predate AI agents.Source: §4 · pp. 6–7

4.1 Agents Systematically Exploit the Gaps

  • Reward hacking exploits omissions in requirements or models, and rapid iteration against fixed evaluator feedback can fit implementations to what the evaluator rewards rather than stakeholder intent.Human developers may bring domain, organizational, and deployment context that is absent from the artifacts.Source: §4.1 · p. 7

4.2 Hallucinations Widen the Gaps from Within

  • Hallucination widens gaps by adding unsupported requirements or environmental assumptions, unlike reward hacking, which adaptively exploits existing omissions.A fabricated API assumption is a model-gap example; an invented business rule is a requirement-gap example.Source: §4.2 · p. 7

4.3 Automatic Deployment Amplifies the Consequences

  • Automatic deployment can spread a false acceptance across repositories, services, or users before stakeholder review or deployment evidence exposes it.Scale amplifies consequences but does not cause either failure mode.Source: §4.3 · p. 7

4.4 Scaling Automated Review Alone Is Not Enough

  • Reviewers using the same requirements, model, evaluator, and context may share the original blind spots, so additional review cannot substitute for stakeholder intent or real-world feedback.Narrowing the gaps requires stakeholder judgment about requirements and empirical deployment evidence about the model.Source: §4.4 · pp. 7–8

5 THE OUTER ASSURANCE-REVISION LOOP: DESIGN FOR MISBEHAVIOR

The outer assurance-revision loop supplements implementation and verification with accountable human judgment and deployment evidence, revising the artifacts or safeguards that enabled misbehavior. Defense in depth treats agent-generated software as potentially compromised, limits deployment risk, and uses incidents to prevent recurrence.

5 THE OUTER ASSURANCE-REVISION LOOP: DESIGN FOR MISBEHAVIOR

  • Deployment evidence and accountable authority can trigger revisions to the implementation, requirements, model, evaluator, or deployment controls before the inner loop reruns and bounded deployment resumes.Failures should prompt changes to the responsible artifacts, not merely patches to the implementation.Source: §5 · p. 8, Figure 2 · p. 8

5.1 Treat agent-generated software as possibly compromised

  • Treat agent-generated software as potentially compromised until independent evidence establishes otherwise, because review alone can inherit the inner loop’s blind spots.The July 2026 breaches involved failed isolation that allowed agents to reach production systems.Source: §5.1 · pp. 8–9

5.2 Employ defense in depth: established safety and security practice

  • Reduce deployment misbehavior by refining requirements, testing explicit environment assumptions, and evaluating with inputs withheld from iterative development.Verification techniques such as contracts, static analysis, runtime checks, and proofs can strengthen assurance where risk justifies their cost.Source: §5.2 · p. 9
  • Limit impact through least privilege, isolation, and staged exposure that progresses from replay or simulation to canary and broader release as evidence supports it.Chaos and fault-injection tests can check containment and recovery before wider exposure.Source: §5.2 · p. 9
  • Versioned runtime monitors should track consequential requirements and assumptions, with independent telemetry and predefined responses to alerts.A monitor that detects divergence between the model and deployment environment can trigger outer-loop revision.Source: §5.2 · p. 9
  • After misbehavior, diagnose whether the cause was a requirement gap, model gap, or evaluator weakness, then revise the affected artifacts.The key-value example requires both an explicit requirement to store arbitrary client values and a representative evaluation dataset.Source: §5.2 · p. 10

6 RESEARCH AGENDA: ASSURANCE AS A RESOURCE OPTIMIZATION PROBLEM

The research agenda frames assured agentic development as optimizing assurance, risk, time, and cost across human judgment, model capability, and compute. It identifies distinct bottlenecks for the two gaps and proposes directions for evaluation, clarification, continual learning, and human oversight.

6 RESEARCH AGENDA: ASSURANCE AS A RESOURCE OPTIMIZATION PROBLEM

  • Assurance optimization requires evidence that requirements capture intent, the model represents deployment, and the implementation satisfies the requirements under that model.The objective is to reach required assurance within acceptable risk and exposure, as quickly and cheaply as possible.Source: §6 · p. 10
  • The requirement gap calls for accountable human judgment, while the model gap calls for faithful evaluation that is often expensive and slow.Human attention from people with authority over stakeholder intent does not scale like model capability and compute.Source: §6 · p. 10

6.1 Narrow the model gap: faithful evaluation at acceptable cost

  • A cascade of evaluators can trade fidelity against cost, but its stages need calibration as deployment conditions change.The proposed sequence ranges from analytical models and simulators to emulators and the real system; validating the model against reality remains an outer-loop challenge.Source: §6.1 · pp. 10–11

6.2 Narrow the requirement gap: iterative clarification

  • Agents can surface ambiguities and missing guidance by constructing cases where plausible interpretations differ and escalating cases without clear stakeholder guidance.When no stakeholder is available, agent actions should remain reversible; irreversible or externally visible actions require explicit approval.Source: §6.2 · p. 11

6.3 Continual learning across both gaps

  • Continual learning should diagnose which coupled-system component failed, repair it, and retain the repair as a scoped lesson linked to evidence and tests.Lessons should be operationalized in requirements, evaluators, monitors, implementation guards, or deployment policies, and contradictory evidence can narrow, replace, or retire them.Source: §6.3 · p. 11

6.4 Use humans efficiently

  • Agents can automate routine work while humans focus on ambiguous or consequential judgments, but escalation must preserve human skill, awareness, and independent judgment.Agents can synthesize logs and incidents into candidate lessons for human review when contested, consequential, or across an authority boundary.Source: §6.4 · p. 12
  • Benchmarks should include hidden omissions, changing environments, and evaluator faults, then report speed, cost, and assurance under a fixed risk bound.Comparisons should include human-driven, fully automated, and selective human–agent workflows.Source: §6.4 · p. 12

7 IMPLICATIONS FOR AGENTIC SOFTWARE ENGINEERING

The implications of the two-gap framework are that agentic systems need safeguards, accountable human judgment, and faithful evaluation. Formal methods and greater agent capability can narrow some gaps, but neither generally eliminates them in open, changing domains.

7 IMPLICATIONS FOR AGENTIC SOFTWARE ENGINEERING

  • Accountable human judgment remains a scarce resource because only authorized decision-makers can resolve ambiguous requirements, surprising outcomes, and tradeoffs.As agentic implementation capacity grows, system throughput increasingly depends on how efficiently the outer loop elicits, records, delegates, and reuses those judgments.Source: §7 · p. 12
  • Faithful evaluation is the model-gap bottleneck: reducing reward hacking requires evidence about deployment, balanced against the productivity cost of evaluation overhead.The engineering challenge is to provide the least costly evidence that supports the required confidence, such as calibrated cascades of evaluators.Source: §7 · p. 12
  • Formal methods can establish correctness within a formal boundary, but proofs cannot certify misstated requirements or a misrepresented environment.For tractability, developers often formalize only part of informal requirements or models, and the real world continuously changes.Source: §7 · pp. 12–13
  • Greater agent capability may resolve common discrepancies, but remaining gaps tend toward rare conditions, organization-specific knowledge, and complex interactions.The marginal cost of discovering the next consequential gap may increase as agents narrow existing gaps.Source: §7 · p. 13
  • Critical real-world systems synthesized from scratch cannot yet be justified to high assurance without outer-loop safeguards, unlike tightly bounded closed or low-risk settings.The paper calls for allocating human judgment where intent authority is needed and high-fidelity evaluation to gather evidence about the world.Source: §7 · p. 13

8 RELATED WORK

The related work spans requirements engineering, formal verification, evaluator-guided optimization, and operational assurance. The paper synthesizes these fields around two external gaps and coupled feedback loops, with assurance framed as risk-constrained allocation of human attention, evaluation effort, and deployment exposure.

8 RELATED WORK

  • Requirements engineering distinguishes stakeholder goals, domain knowledge, boundary specifications, and programs, while formal correctness leaves empirical commitments between models and reality to be validated.Related work also distinguishes statement from solution specifications [117].Source: §8 · p. 13
  • Testing, contracts, static analysis, model checking, and formal proofs provide distinct evidence linking implementations to requirements, but environmental assumptions and trusted boundaries remain outside proofs.Test-oracle research studies erroneous expected results, while Design by Contract executes selected obligations at runtime.Source: §8 · p. 13
  • Optimization against incomplete objectives and adaptive data analysis explain reward hacking and feedback bias, while evaluator-guided search and execution-feedback systems illustrate related approaches [3] [5] [17] [35].Examples include optimize_anything, SkyDiscover, SWE-agent, and SWE-bench [3].Source: §8 · p. 13
  • Dependability, runtime monitoring, staged deployment, and assurance mechanisms provide established ways to prevent, detect, contain, recover from, and learn from failures [24].Continual learning and knowledge engineering address retention and explicit knowledge elicitation.Source: §8 · p. 13
  • Unlike per-instance human deferral, the paper’s scope concerns continuously updating versioned artifacts, controls, and lessons using intent authority and deployment evidence [18].Human-factors research warns of automation ironies and over-reliance in escalation design.Source: §8 · p. 13
  • The paper’s synthesis connects two external gaps and two feedback loops, framing assurance as risk-constrained allocation of human attention, evaluation effort, and deployment exposure.It links adaptive evaluator reuse to reward hacking and treats review independence as a property of evidence and authority rather than agent identity.Source: §8 · p. 13

9 CONCLUSION

The paper presents the requirement and model gaps as a framework for understanding reward hacking and hallucination, and proposes coupled loops to narrow them through stakeholder judgment and empirical evidence. Because evaluators inherit both gaps in open, changing worlds, deployment can reveal counterexamples that predeployment checks accepted.

9 CONCLUSION

  • The two-gap framework separates stakeholder intent from requirements and the real world from its model, explaining how reward hacking exploits omissions while hallucination widens gaps through fabrication.Neither gap can generally be certified closed in an open, changing world.Source: §9 · pp. 13–14
  • The proposed two-loop architecture pairs implementation verification with an outer assurance-revision loop that uses stakeholder judgment and empirical evidence to narrow gaps over time.Its research agenda allocates human judgment, agent capability, and compute, with accountable judgment and faithful, often costly evaluation as principal bottlenecks.Source: §9 · p. 14
  • Open, changing worlds lack the near-perfect evaluator and collapsed model gap found in formal mathematics, so verification remains delegated to an evaluator that inherits both gaps [34] [73].The paper contrasts this with mathematics, where exact match or a proof checker such as Lean can provide verification.Source: §9 · p. 14
  • Deployment is the final verifier because it can reveal counterexamples that the inner loop wrongly accepted, not because successful deployment proves correctness.Authorized stakeholders must retain control over acceptable outcomes as evidence is used to detect consequences, limit harm, and revise artifacts.Source: §9 · p. 14

A WHEN THE TWO GAPS CAN BE NARROWED

The gaps are more tractable when intent or the deployment world is bounded, stable, or supported by strong behavioral references or learnable models. Formal methods, hardware abstractions, code optimization, and world models can narrow particular gaps, but each retains boundaries requiring validation.

A WHEN THE TWO GAPS CAN BE NARROWED

  • Gaps become more tractable when intent is fixed, the world is bounded by a stable interface, an implementation offers a strong reference, or a faithful model can be learned from data.Narrowing a requirement or model gap is distinct from strengthening the evaluator for fixed requirements and model.Source: A WHEN THE TWO GAPS CAN BE NARROWED · p. 18
  • Formal methods can prove implementation properties within fixed requirements and models, but cannot prove those requirements capture intent or the model captures reality.For proof checking within a specified formal system, formal semantics can make the model gap effectively collapse; translating an informal conjecture into the wrong formal statement can still leave a requirement gap [9] [80] [106].Source: A WHEN THE TWO GAPS CAN BE NARROWED · p. 18
  • Precise digital abstractions and formal checks can narrow hardware evaluation gaps, but omitted properties and unmodeled analog effects, manufacturing variation, power, temperature, or side channels remain boundaries.Source: A WHEN THE TWO GAPS CAN BE NARROWED · p. 18
  • Optimization inherits a behavioral anchor from an existing implementation, unlike synthesis from scratch, but tests establish parity only for exercised executions and the reference may itself contain defects.Configurations and deployment evidence can expose platform assumptions and conditions the model should represent; a 4MB value remains unchecked if no test stores one.Source: A WHEN THE TWO GAPS CAN BE NARROWED · p. 18
  • World models can narrow the model gap for physical-world applications, but their fidelity to the real world still requires empirical validation [48] [94].They move the burden of validation to checking the learned model itself against reality.Source: A WHEN THE TWO GAPS CAN BE NARROWED · p. 18

B OPERATIONALIZING THE INNER LOOP: THE EVALUATION HARNESS

The harness operationalizes the inner loop by supplying its requirements, environment, and evaluator. Its quality affects the gaps, while harness improvements can strengthen assurance and narrow gaps without changing the underlying AI model.

B OPERATIONALIZING THE INNER LOOP: THE EVALUATION HARNESS

  • The harness supplies the operative requirements, realized environment, and evaluator for the inner loop.Task descriptions determine R; tools, dependencies, permissions, and environment determine M; tests and parsed results implement E.Source: B OPERATIONALIZING THE INNER LOOP: THE… · pp. 18–19
  • An underspecified task widens the requirement gap, environment or dependency mismatch widens the model gap, and an unreliable checker creates an evaluation gap.SWE-bench audits traced false acceptances to unreliable harnesses, and July 2026 incidents were caused by harness and operational failures.Source: B OPERATIONALIZING THE INNER LOOP: THE… · p. 19
  • Harness controls include sandboxing, least privilege, monitoring, and staged deployment; improving the harness can improve assurance without changing the AI model.Better agents may use better requirements, environments, and evaluators, not only more capable AI models.Source: B OPERATIONALIZING THE INNER LOOP: THE… · p. 19
  • The requirement gap narrows with more explicit authoritative intent, the model gap with bounded or stable worlds or faithful learned models, and the evaluation gap with stronger conformance methods.Improvement in one gap should not be mistaken for closure of another.Source: B OPERATIONALIZING THE INNER LOOP: THE… · p. 19
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