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The Elephant-Goldfish Model (EGM) is a spec-driven workflow that separates software design from specification checking. A long-running “Elephant” session keeps the project’s architecture and history in view, while one or more fresh “Goldfish” sessions receive only the written design and challenge it for ambiguity, omissions, edge cases, and implementation gaps. Code is written only after the design passes those checks. The separation is intended to prevent a single AI conversation from carrying forward untested assumptions and context pollution into production code.
EGM is an open-source workflow, not a code-generation model or a guarantee of defect-free software. Its practical value is qualitative: it gives teams deliberate comprehension, adversarial, and implementation-readiness gates before an AI coding agent starts editing files.
What is the Elephant-Goldfish Model?
EGM treats an AI-assisted project as two different jobs: designing the system and independently checking whether the design is understandable and buildable. The Elephant is a context-rich, long-horizon session that holds architectural intent, project memory, constraints, and decisions. It brainstorms with the human and records the result in a living design document; it is not supposed to write the implementation.
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A Goldfish session is deliberately stateless. It starts with no conversational history and receives the specification as its working context. Because it does not remember the hypotheses, shortcuts, or arguments that produced the document, it can read the text as a skeptical new engineer would. The official repository describes the pattern as deep-context Elephant sessions drafting specifications and isolated zero-context Goldfish subagents stress-testing them before code is written.
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The name is a mnemonic for the information boundary, not a claim about model intelligence. An Elephant may be the same underlying model used in a Goldfish session; what changes is the context and role. The workflow can therefore be applied to different coding-agent harnesses rather than requiring a special model.
Google’s Open Source Blog introduced the approach on October 22, 2026, using the phrase “Design is the new code.” The project is open source under the Apache 2.0 license and is not an officially supported Google product.
How does EGM find mistakes before implementation?
EGM uses a sequence of reviews, each designed to expose a different class of failure. The order matters: a design must first be understood, then attacked, then checked for enough detail to implement.
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A fresh Goldfish reads the complete design document and explains the architecture, data flow, interfaces, and intended behavior back to the team. If two reasonable readers would produce different explanations, the document is underspecified. The Elephant session then resolves the ambiguity in the document instead of relying on an unwritten conversation.
2. Antagonistic review
Additional Goldfish reviewers act as skeptical technical critics. They look for missing failure paths, contradictory requirements, hidden dependencies, unsafe defaults, race conditions, migration problems, and assumptions about external systems. They are not asked to be agreeable or to preserve the original plan; their output is a list of findings tied to specific parts of the specification.
3. Implementation-readiness check
A final Goldfish verifies that an engineer or coding agent can act on the document without guessing. It checks that file paths, modules, data structures, function signatures, error behavior, configuration changes, and acceptance criteria are explicit. A design can be conceptually sound yet fail this gate if it never says where a change belongs or what an interface returns.
These checks target the failure mode the project calls “Hallucination Cascades & Context Pollution.” In an unstructured conversation, an early speculative idea can be repeated as if it were a fact, then influence every later design and code decision. EGM makes the written specification the hand-off artifact, so unsupported assumptions have to survive an independent reading.
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Why use a fresh Goldfish session instead of asking the Elephant to review its own plan?
A fresh session has no incentive or conversational pressure to defend the plan it helped create. It also cannot silently fill gaps with details that were discussed earlier but never recorded.
Long AI conversations accumulate provisional hypotheses, discarded alternatives, and implicit agreements. The model may treat that accumulated context as authoritative even when the final document is incomplete. A zero-context Goldfish sees only what a future implementer will see: the specification and any explicitly supplied project files or constraints.
The isolation is not a claim that Goldfish output is automatically correct. Goldfish reviewers can misunderstand a clear design, overlook a subtle defect, or raise issues that do not apply. Human designers must adjudicate findings and update the document. The benefit is that misunderstandings and objections appear before implementation rather than after an agent has spread them through a codebase.
What does the EGM workflow look like in practice?
The repository’s quickstart uses four commands and a human resolution loop. Names begin with /egm-; exact availability depends on the runtime installation.
- Brainstorm. Start an Elephant session with
/egm-brainstorm. Define the user problem, constraints, non-goals, dependencies, and open decisions. Keep this phase focused on intent and architecture, not implementation patches. - Draft the living design document. Use
/egm-draftto turn the discussion into a versioned specification. Record interfaces, data models, state transitions, error handling, security considerations, operational assumptions, and testable acceptance criteria. Treat the document as the source of truth that will be handed to Goldfish reviewers. - Review in isolation. Run
/egm-reviewto submit the document to fresh Goldfish sessions. Ask one pass to restate the design, other passes to search for adversarial edge cases, and a final pass to verify implementation readiness. Keep each reviewer’s context limited to the material an implementer is meant to receive. - Resolve findings. The human team decides which findings are valid, changes the design document, and reruns the relevant reviews. Do not patch code to work around an unresolved specification conflict; update the stated intent first.
- Implement only after verification. Give the approved design to the coding agent or engineer. The implementation should be traceable to the document’s functions, files, and acceptance criteria.
The setup guide describes the workflow as flexible and keeps human review essential. Teams can vary the number of reviewers, split reviews by subsystem, or add organization-specific checklists without changing the core context boundary.
What is the difference between an Elephant and a Goldfish session?
The roles are complementary rather than competing. The following distinctions explain why EGM uses both.
| Dimension | Elephant | Goldfish |
|---|---|---|
| Context | Retains project history, prior decisions, and architectural vision. | Starts with zero conversational memory and reads the supplied specification. |
| Primary role | Co-designs the solution and records intent. | Audits the written specification for comprehension, risks, and gaps. |
| Typical timing | During discovery and design; it can continue to maintain the document. | Before implementation, with later checks possible to detect drift. |
| Output | A living design document and clarified decisions. | Restatements, findings, questions, and an implementation-readiness judgment. |
| Failure it is meant to reduce | Unrecorded assumptions and architectural drift during exploration. | Blind spots caused by conversational familiarity and context pollution. |
The table describes role boundaries, not separate products. A team may use the same model family for both sessions as long as the Goldfish invocation receives a clean, deliberately chosen context.
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What should an EGM design document contain?
A Goldfish can only test what the document states. A useful specification gives reviewers enough detail to reason about behavior without needing the Elephant’s private conversation.
- Goal and non-goals: define the user outcome and explicitly exclude tempting adjacent features.
- System shape: identify components, responsibilities, trust boundaries, external services, and the direction of data flow.
- Interfaces: specify function or API signatures, input validation, return values, error types, timeouts, and compatibility rules.
- Data and state: describe schemas, invariants, lifecycle transitions, idempotency, persistence, and migration behavior.
- Operational behavior: state logging, metrics, retries, rate limits, configuration, deployment assumptions, and rollback plans.
- Security and privacy: identify authentication, authorization, secret handling, sensitive fields, and abuse cases.
- Acceptance criteria: give observable examples, negative cases, and tests that demonstrate completion.
- File-level plan: name the paths to create or modify and explain how each change connects to the architecture.
If a decision remains open, label it as open and give the options and decision owner. Hiding uncertainty is more dangerous than documenting it because a reviewer may otherwise treat a guess as a requirement.
What kinds of AI-code mistakes can Goldfish reviewers expose?
Goldfish review is most valuable where a plausible implementation could still violate an unstated rule. Typical findings fall into several categories.
Ambiguous behavior
Terms such as “recent,” “active,” or “eventually consistent” need measurable definitions. A reviewer may ask which timestamp is authoritative, how clock skew is handled, or what a user sees during propagation.
Uncovered edge cases
Reviewers can probe empty inputs, duplicate messages, partial failures, retries, concurrent updates, expired credentials, oversized payloads, and service outages. They can also test boundary values that ordinary examples omit.
Hidden dependencies
A design may assume a database constraint, environment variable, queue ordering guarantee, third-party API behavior, or existing helper function without naming it. The implementation-readiness pass forces those dependencies into the document.
Contradictory requirements
Performance, consistency, privacy, and backward compatibility goals can conflict. Separate reviewers often notice that one section requires strict ordering while another permits asynchronous processing, for example.
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Unsafe or incomplete failure handling
A happy-path design may not say what happens after a timeout, failed write, malformed response, or restart. Goldfish findings should turn each relevant failure into a specified response, retry policy, alert, or user-visible error.
These are categories of issues the workflow is intended to surface, not a measured defect-removal rate. The published material provides no controlled benchmark, bug-reduction percentage, or comparative study, so numerical claims about EGM’s effectiveness would be unsupported.
Can EGM be used with Codex, Claude Code, Antigravity, or ChatGPT Work?
Yes. The setup guide names Antigravity, Claude Code, OpenAI Codex, and ChatGPT Work as runtime targets. EGM is a plugin and workflow installed through a repository or agent commands, so compatibility depends on whether the target environment can run the supplied commands and pass a clean design document to a new session.
For any runtime, preserve the same discipline: keep brainstorming and design in the context-rich session, start Goldfish reviewers without that conversational history, and include every file or constraint the reviewer is expected to consider. If a harness automatically shares prior messages, create a genuinely new session or use an isolated worker mechanism rather than merely opening a new chat turn.
The named environments are targets, not an endorsement or guarantee of feature parity. Check each environment’s current command-loading and session-isolation behavior before adopting EGM in a production process.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can teams prevent context leakage during review?
Context leakage defeats the main control EGM is trying to create. Use a written hand-off and make the boundary auditable.
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- Export the design document at a specific revision and give reviewers that revision, not an evolving chat transcript.
- Exclude brainstorming logs, rejected alternatives, and the Elephant’s private reasoning unless the document intentionally records them as requirements.
- Provide repository files, schemas, and API contracts explicitly; “the agent knows the codebase” is not a substitute for a defined input set.
- Label reviewer roles and prompts so a comprehension pass does not accidentally receive another reviewer’s conclusions.
- Record each finding against a document section, resolve it in the document, and rerun review after material edits.
After implementation, compare the code and operational behavior with the approved specification. A later Goldfish pass can look for drift—changes that quietly alter interfaces, invariants, or acceptance criteria—even though the principal gate occurs before coding.
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What are EGM’s limits and trade-offs?
EGM adds review time and token cost, and it can produce false positives. Multiple reviewers may disagree, especially when the specification intentionally leaves a product decision open. Humans still have to choose the design, validate security and compliance obligations, and inspect the resulting code.
Isolation also reduces useful background. A Goldfish that lacks repository details may flag a dependency that is actually guaranteed by an existing module, or miss a problem hidden outside the supplied files. The remedy is not to restore the entire Elephant transcript; it is to improve the explicit review packet with the relevant contracts and constraints.
Finally, EGM does not prove correctness. It does not replace unit tests, integration tests, static analysis, threat modeling, staged deployment, monitoring, or production rollback procedures. It is a pre-implementation specification control that makes those later activities more focused.
When is the Elephant-Goldfish Model worth adopting?
EGM is most useful for changes where a misunderstood requirement is expensive: cross-service features, data migrations, security-sensitive flows, public APIs, infrastructure changes, and long-lived systems with many contributors. The written design also helps when work will be handed from one agent or team to another.
A tiny, local change may not justify several review passes. Teams can scale the process by using a short design and one Goldfish comprehension check for low-risk work, then adding adversarial and readiness passes as the blast radius grows. The governing question is whether the cost of clarifying the design now is lower than the cost of debugging an assumption after code and data have changed.
What does EGM actually guarantee?
It guarantees a process distinction, not a software outcome: the design is exposed to readers who did not participate in creating its conversational context. The official material supports a qualitative expectation of earlier detection for ambiguity, omissions, edge cases, and context-related failures, but it reports no efficacy statistic or controlled comparison. Treat EGM as one layer in an engineering system whose final reliability still depends on human decisions, implementation quality, and testing.
Frequently Asked Questions
Does EGM replace human code review?
No. Human reviewers decide which Goldfish findings are valid, approve trade-offs, and inspect the implemented code. EGM moves an additional review gate earlier, before design assumptions have become code and data migrations.
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There is no prescribed number in the published workflow. Use enough independent passes to cover comprehension, adversarial risk, and implementation readiness; a small change may need one pass, while a high-impact system can assign separate reviewers to security, data, and operations.
Can a Goldfish session review an existing codebase?
Yes, if the session is given the relevant code and an explicit task, but EGM’s defining gate is specification review before implementation. A post-implementation pass is useful for detecting drift between the approved design and the code that was actually delivered.
Is EGM tied to a particular AI model?
No. The method separates context and responsibilities rather than requiring a named model. The setup guide lists several runtime targets, and teams can apply the same hand-off rules wherever they can create isolated sessions.
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