How to validate a specification before handing it to another coding agent in 2026

Treat a specification made by one coding agent as an input the receiving agent must accept: test it on representative work and required checks, compare direct transfer with an adapted or selectively retrieved representation, and retain the handoff only when the receiver meets the same acceptance criteria. A specification can be complete enough for its author and still be a poor working interface for the agent asked to implement it.

This is an AgentOps Hardening recommendation, not a claim that every cross-agent handoff fails. The direct evidence is one arXiv v1 preprint submitted in August 2026, not a peer-reviewed production study. It evaluates Oracle-to-PostgreSQL migration, so it supplies a test pattern—not a universal ranking of coding agents or specification formats.

1,802

Oracle scripts in the evaluated migration setting

3

adaptation families tested after direct transfer

v1

unreviewed preprint evidence

1

controlled migration domain—not a general portability result

A specification is an interface, not proof of compatibility

The paper compares a specification native to the receiving environment with one made elsewhere, then evaluates generated migration output with token, syntax, AST, and runnability measures. In that setting, direct transfer changed outcomes in agent-dependent ways; specification size alone did not explain output quality.

A handoff artifact can preserve facts chosen by the authoring agent while still presenting them in a form the next agent does not use well. The relevant question is whether the receiver can turn that representation into an acceptable implementation under the checks that matter.

Put a receiver-side acceptance gate around the handoff

Before treating a cross-agent specification as reusable, define a small receiver-side gate. This is a Bonfire operating recommendation derived from the paper's mechanism—not a standard validated by its authors.

Make that boundary explicit in an agent workflow graph. A successful handoff is not “the specification arrived”; it is a falsifiable result: this receiver produced an acceptable implementation from this representation on comparable work.

Test representations instead of canonizing one format

After direct transfer, the paper tests rewrite, compression, and retrieval-augmented ingestion. Rewriting improved Gemini in the observed Kiro-to-Gemini configuration. Compression was not a universal improvement. Retrieval-augmented ingestion appeared on the reported per-agent Pareto frontiers for Gemini and Copilot, without winning every metric.

1. Run a small direct-transfer baseline.
2. Try a receiver-oriented rewrite that preserves requirements, decisions, dependencies, and open questions.
3. Try a scoped retrieval view when the whole artifact would be distracting or too broad for the next task.
4. Keep only the representation that meets the acceptance evidence at an acceptable operating cost.

Use checks that resemble the actual decision

The study separates syntax, structural similarity, and immediate runnability. Its authors explicitly frame runnability as a lower bound: scripts ran in an empty experimental database with generated stubs, not an exact production schema. A permissive smoke check can show that a receiver produced something executable without proving it preserved a business rule, integration assumption, or deployment constraint.

Choose acceptance evidence at the boundary the next agent will cross: relevant tests, focused review of requirements it must preserve, and any policy or interface checks that would reject a bad implementation. If the handoff adds a reviewer, evaluate whether it changes the accepted implementation, not merely its critique; see how to evaluate an AI agent reviewer.

Preserve the limits of the evidence

The paper does not establish that specifications made by another agent are inherently worse. It reports agent-dependent behavior in one controlled Oracle-to-PostgreSQL migration setting, with observed agents, ingestion strategies, datasets, and metrics. Use it as a design warning, not a vendor verdict: validate the receiver's interpretation when an agent boundary matters.