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The Elephant-Goldfish Model (EGM) aims to catch mistakes before an AI coding agent writes code: one context-rich session develops the design, then separate, fresh sessions challenge the written specification without inheriting the designer’s conversational history. Those Goldfish reviewers check whether the design is understandable, complete, and ready to implement. The approach is intended to expose ambiguity, omissions, edge cases, and context-related errors early; official sources do not publish a controlled benchmark or a bug-reduction percentage proving how effective it is.
What is the Elephant-Goldfish Model?
EGM is an open-source, spec-driven workflow for AI-assisted software development. It separates two jobs that can otherwise blur together in one long conversation: developing a project’s design and independently checking whether that design is clear enough to build.
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Google’s Open Source Blog described the Elephant as holding “the end-to-end architectural vision and project memory” and Goldfish sessions as “stateless, zero-context sessions used strictly to validate specifications” in an announcement dated October 22, 2026. The project repository likewise summarizes the method as deep-context sessions drafting specifications, followed by isolated subagents stress-testing them before code is written.
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How can a reviewer with less context find more mistakes?
A designer who has spent hours discussing a feature may unconsciously fill gaps in its written specification. The author knows what a vague phrase was meant to mean, remembers a constraint mentioned earlier, or assumes a dependency is obvious. A new reviewer cannot rely on that private history. If the design does not explain a decision, the Goldfish must either flag the gap or expose that the instruction can be read in more than one way.
This is the central trade-off: the Elephant’s context helps it preserve project intent, while the Goldfish’s lack of conversational context helps it judge what the document itself communicates. EGM does not treat one session as both author and final authority. It moves the design across that boundary so the specification can be tested on its own terms.
The architecture document identifies “Hallucination Cascades & Context Pollution” as systemic failure modes in unstructured AI code generation. In practice, a long conversation can contain tentative ideas, rejected approaches, and later corrections. An agent may carry an outdated assumption forward or treat a speculative suggestion as a settled requirement. A review based on the written design rather than the whole conversation is meant to make missing decisions visible before they become implementation assumptions.
What does Goldfish review check?
The workflow describes three checks, moving from whether the design can be understood to whether it gives an implementer enough detail. Each check addresses a different kind of specification failure.
1. Can a fresh reviewer explain the design?
A Goldfish reads the complete design document and explains its architecture and intent back. If reviewers cannot agree on what the system is supposed to do, or cannot explain how its parts fit together, the document is not yet communicating reliably. The remedy is to revise the specification, not ask the reviewer to guess what the Elephant meant.
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For example, a hypothetical requirement that says “retry failed requests safely” leaves important questions open: which failures qualify, how many attempts are allowed, and what makes a retry safe? A reviewer’s questions are evidence that the document needs to settle those decisions. This example illustrates the kind of ambiguity the process targets; it is not a reported EGM test result.
2. What could go wrong at the edges?
Multiple Goldfish reviewers act as skeptical technical critics. They look for edge cases, hidden dependencies, and logical gaps rather than simply endorsing the author’s preferred design. One reviewer may notice that an error path is undefined; another may find that a stated behavior depends on data or a service the document never describes.
The point is not that reviewers will discover every defect. Their findings give the human and Elephant concrete questions to resolve while changes are still design changes, rather than assumptions already embedded in code.
3. Is the design ready to implement?
A final Goldfish checks whether implementation details are present and unambiguous, including file paths, data structures, and function signatures. A design may express the right feature in broad terms yet leave an engineer or coding agent to invent how its pieces connect. This readiness check is intended to identify that gap before implementation starts.
These checks are related but not interchangeable. A reviewer might understand the high-level architecture and still find an undefined edge case; a document might cover the edge cases but omit the signature or file location needed to implement a component. EGM treats comprehension, adversarial critique, and implementation readiness as separate review questions.
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How do you use EGM before code is written?
The repository quickstart gives a five-part sequence: brainstorm the work, draft a living design document, review it, resolve findings, and submit the verified design for implementation. The named commands are slash commands; the setup guide says the workflow is flexible, so treat this as the repository’s quickstart sequence rather than a universal requirement for every project.
- Brainstorm with
/egm-brainstorm. Use the Elephant session to explore the feature, constraints, and architectural intent. Keep unresolved options distinguishable from decisions so the resulting design does not silently turn speculation into requirements. - Draft with
/egm-draft. Have the Elephant create or update the living design document. The document is the handoff between design and review; it should state the intended behavior and the architectural choices the reviewers need to assess. - Review with
/egm-review. Send the written design through Goldfish review. The important separation is that fresh reviewers assess the specification without inheriting the Elephant’s conversation, rather than asking the authoring session to confirm its own assumptions. - Resolve the findings. Work through questions and criticism with human judgment. Clarify the document where it is ambiguous, add missing decisions or implementation details, and do not treat a review as a pass merely because a reviewer produced an answer.
- Submit the verified design for implementation. Once the design is understandable and its implementation details are sufficiently clear, use it as the basis for coding. The workflow places this review before implementation so unresolved design questions are not left for the coding agent to guess.
The setup guide emphasizes that human reviews remain essential. EGM structures scrutiny; it does not transfer responsibility for approving architecture, deciding trade-offs, or judging whether a finding matters. If a reviewer raises a concern, a human still has to decide whether to change the design, document why the behavior is intentional, or investigate further.
What changes between Elephant and Goldfish?
The roles differ across context, responsibility, timing, and output. The separation matters because a review is useful only if its input and purpose are clear.
| Dimension | Elephant | Goldfish |
|---|---|---|
| Context | Retains project history and architectural vision. | Starts without inherited conversational memory; reads the specification it is asked to review. |
| Role | Co-designs and records intent. | Audits the written specification for comprehension, gaps, edge cases, and readiness. |
| Timing | Works during design; implementation follows review. | Reviews the design before code is written. The project also describes drift detection continuing after implementation, without specifying its mechanics in the cited overview. |
| Output | A design document. | Findings and readiness judgments about that document. |
Can you use EGM with Codex, Claude Code, or Antigravity?
The setup guide names Antigravity, Claude Code, OpenAI Codex, and ChatGPT Work as runtime targets. That means the guide identifies them as environments for the workflow; it does not establish that every command or integration behaves identically across all versions or configurations.
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The project is open source under the Apache 2.0 license, and its repository says it is not an officially supported Google product. Google’s announcement explains the model, but readers should distinguish that coverage from product support: the project’s own status statement does not make EGM an officially supported Google service.
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The available setup information does not establish a required programming language, a guaranteed compatibility matrix, or a particular AI model version. Before adopting the workflow in a team, check the project’s setup instructions for the runtime you actually use and confirm that its commands and review handoffs work in that environment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What can EGM reliably claim—and what remains unproven?
The defensible claim is about intent and process: EGM is designed to expose ambiguity, omissions, edge cases, hidden dependencies, logical gaps, and context-related failures earlier by separating specification authorship from isolated review. Those are useful targets because they can be addressed in the design document before a coding agent turns an assumption into implementation.
That is not the same as evidence that EGM reduces bugs by a particular percentage, outperforms another workflow, or catches every important error. The official sources do not provide a controlled benchmark, a comparative study, a published bug-reduction rate, or a measured error rate. Nor is an adoption statement a performance result. The announcement uses qualitative language about reliability and mentions “thousands of Googlers,” but that statement should not be read as independent proof of efficacy.
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When is this workflow useful?
EGM is most relevant when a change has enough architectural or behavioral complexity that a coding agent could plausibly fill gaps with assumptions. A separate specification review can be especially valuable when requirements have several edge cases, when components or services depend on one another, or when an implementation must follow decisions already made elsewhere in the project. These are practical use cases implied by the review targets, not a claim that the project has measured better outcomes for those categories.
For a tiny, self-contained change whose behavior and implementation are already unambiguous, the full sequence may be more process than the task needs. The setup guide explicitly presents the workflow as flexible. A team can use that flexibility to decide how much design and review is proportionate, while preserving the underlying distinction: do not confuse the designer’s remembered intent with what the written specification actually says.
EGM is therefore best understood as a quality gate for specifications, not a code-generation model, an automatic correctness proof, or a substitute for testing and human engineering judgment. Its distinctive contribution is to make the design document answerable to a fresh reader before the implementation begins.
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Frequently Asked Questions
Does EGM require a specific programming language?
The official setup information summarized for EGM does not state a language requirement. That does not guarantee compatibility with every project or toolchain; check the project’s current setup instructions for the runtime you plan to use.
Does EGM test code after it has been written?
The core sequence described by the repository reviews the design before implementation, rather than presenting Goldfish as a test runner. The project also refers to drift detection continuing after implementation, but the cited overview does not define that mechanism.
Is EGM a Google product?
The project repository says EGM is not an officially supported Google product, despite Google’s Open Source Blog describing the model. Its open-source status and the distinction between an announcement and official product support are important when evaluating support expectations.
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