Grit Labs is a framework designed to support decision-making. It is not a rigid process nor an exact science. Rather, it is a high-level conceptual model for how ideas become systems.
Grit Labs works from the premise:
Program to an interface, not to an implementation — across all levels, from code to communication.
In Grit Labs, an interface is more than just a language feature. It is any system or specification where the patterns below are observed:
Predefined Structure – What elements exist and how they are organized structurally.
Consistent Naming – For example: names of fields, objects, actions, and events stay the same. Namebinding techniques are common.
Relationships – The structural or semantic links that give context and meaning to how concepts, artifacts, or entities interact.
Constraints – Rules about what is valid or allowed.
Types - The form and structure of data. These include simple types like text, boolean, or number, and complex domain types like Employee, Transaction, or Event.
This interface-based perspective makes ideas concrete. It helps separate concerns, bridge disciplines, and turn abstract thinking into durable, testable outcomes.
For example, Test-Driven Development (TDD) can be viewed as an interface when we observe:
| Field Name | Data Type | Constraints |
|---|---|---|
| Failing Test Description | Text | Nullable, 5–500 characters |
Pass/Fail (passed) |
Boolean | Not Null (required) |
| Test Id | Integer | Not Null (required) |
These definitions form an interface that consistently guides TDD by clearly specifying structure and constraints.
Process Over Results:
Outcomes are unpredictable—external factors will always influence results. But the well-defined elements of an interface provide adaptability, giving teams something to rely on when uncertainty strikes.
Interfaces Encourage Agreement:
When interfaces are well-defined, expectations are shared. This clarity reduces ambiguity, strengthens unity, and provides space for productive collaboration.
A Shared Language Accelerates Work:
Consistent terminology and structured workflows eliminate friction. They reduce misunderstandings, prevent rework, and enable more predictable progress.
Consistency Over Perfection:
Perfect methodologies don’t exist. Only consistent ones do. A bad convention becomes a good one when everyone follows it.
Grit Labs organizes development across three core interface layers—each one building on the clarity and structure of the layer before it.
These interfaces define how understanding is recorded and shared. It starts with the questions:
How do we make actions traceable and auditable?
What are the limits of what we can know—and why do those limits exist?
How do we document what we're building, why it matters, and how we structure that documentation?
How do we divide knowledge among different audiences, and what is an appropriate level of detail?
How does language shift across domains, and what knowledge is proprietary or open?
In place of ad hoc documentation habits, Knowledge Interfaces provide a durable foundation for shared understanding.
Documents provide the source of truth for planning and shared intent—guiding design before code exists. At its core, this interface is a simple, structured sequence:
Structured documents use formal formats—such as XML, JSON, or YAML—to represent information in a consistent, machine-readable way. This allows documents to include metadata, follow validation rules, and remain predictable across tools and teams.
In the context of Grit Labs, we can define a language to support this kind of structure—Grit Structured Language (GSL) . GSL would provide a lightweight way to describe system elements such as interfaces, behaviors, and constraints in a format that is both human-readable and suitable for automation.
Behavior Interfaces build directly on the structured understanding captured through Knowledge Interfaces — using it to define behavior before implementation. They provide external-facing agreements that validate, guide, and constrain development from the outside in.
Acceptance Tests define what the system should do based on observable outcomes. They are derived from user-facing goals and serve as concrete checks that validate system behavior against expected results. Defined before implementation, they act as external-facing contracts that guide and constrain development.
These tests follow the principles of Black Box Modeling—focusing on what goes in, what comes out, and how the system behaves at its boundaries. This keeps attention on what matters to the user, rather than how the system works internally.
To make behaviors verifiable, we organize Acceptance Tests around structured elements:
User Story – a high-level goal from the user’s perspective
→ “As a user, I want to reset my password...”
Acceptance Criteria – specific conditions that must be met for the story to be complete
→ “The user can request a password reset...”
Scenario – a concrete situation that expresses how the system should behave under specific conditions
→ “Given a valid email address, when a user submits the form, then the system sends a password reset email.”
Test Case – an independently verifiable check that validates a single aspect of system behavior
→ Typically structured as a checklist of observable outcomes
Example: Password Reset Flow
The following acceptance test captures the behavior expected when a user attempts to reset their password. It begins with a user story, defines the required criteria, expresses key scenarios, and concludes with testable outcomes.
User Story
As a user, I want to reset my password so that I can regain access to my account if I forget it.
Acceptance Criteria
The system should satisfy the following:
The user can request a password reset by entering their registered email.
An email with a password reset link is sent to the user’s address.
The reset link expires after 24 hours.
The reset link opens a secure form to set a new password.
The new password must:
Be at least 8 characters
Contain at least one number
Include one uppercase letter
Include one special character
Upon successful reset, the user receives confirmation and can log in using the new password.
Scenarios
Given a registered email is entered, when the reset form is submitted, then a reset email is sent.
Given a reset link is older than 24 hours, when a user clicks it, then the system shows an expiration error.
Test Cases
When a registered email is submitted, the system sends a password reset email.
If an unregistered email is submitted, the system displays a generic message.
The password reset link expires after 24 hours.
Clicking the reset link opens a secure page with a password input form.
The form enforces password complexity requirements.
Submitting a valid password updates the account and confirms success.
After resetting, the user can log in using the new password.
Using an expired or reused reset link shows an error message.
These interfaces define the architectural boundaries of the system, rooted in domain concepts. They embody business rules, enforce constraints, and support a structure that can endure change.
Use the language and structure of the domain to guide system design. Emphasize bounded contexts, domain events, and clarity of roles.
Note: DDD is a broad discipline with patterns and practices that go well beyond the scope of this document. What follows is a simplified example to illustrate the spirit of the approach.
Example :
In a subscription billing platform, “Invoice” and “Payment” might exist within a “Billing” bounded context, while “User” and “Authentication” belong to an “Identity” context. Each context models its own rules, events, and data flows without leaking concerns into the other.
The Release Cycle is a structure interface that defines how code moves from development to production in a clear, repeatable way. It ensures that even small teams can deliver reliably, communicate clearly, and keep technical debt low.
This interface is lightweight by design, intended to minimize overhead while preserving clarity and consistency.
Default rhythm: Once per sprint (2 weeks)
Optional: Mid-sprint deployments allowed when value is clear and risk is low
The team plans for one coordinated UAT deployment each sprint. Earlier deployments may occur if features are ready and feedback is needed.
Dev/Main – Daily merges; auto-tested; always deployable
UAT – Shared environment for validation and feedback
Production – Live system, deploy only from approved versions
Environments act as gates in the release flow. Each one increases visibility and confidence.
Format: Semantic Versioning (MAJOR.MINOR.PATCH)
Optional Metadata: +revX (e.g. 1.3.2+rev4) for internal clarity
Tagging: All production deployments are tagged and traceable
A consistent version format simplifies troubleshooting, rollback, and communication.
UAT → Prod Criteria:
Who Approves: Developer + one peer/stakeholder
This shared understanding reduces ambiguity and ensures no surprises in production.
Brief summary of changes
Version tag included
Notes shared in internal team space (e.g. Slack, Notion, or README)
Release notes serve as a lightweight communication tool—not just a log.
This simple release cycle interface aligns with Grit Labs principles:
By using this structure interface, small teams can ship with confidence, learn from their cycles, and adapt with minimal friction.
Define the System through Documents - Use structured documents to capture shared understanding before code exists
→ documents program people
(DocDD + GSL )
Specify Expected Behavior - Define user stories, acceptance criteria, and black box scenarios before implementation
→ interfaces define contracts
(Behavior Interfaces )
Model the Domain - Design system structure based on domain rules, bounded contexts, and architectural clarity
→ models give form to intent
(DDD + Model Interfaces )
Implement and Trace - Write code that satisfies behavior contracts and aligns with modeled structure
→ code programs machines
(Unit Tests + Versioned Releases )
As the system changes, update documents, tests, and models together—so that intent, behavior, and structure remain in sync.
Grouped by concern: Developers, product leads, and designers can all participate based on the layer they're in—behavior, knowledge, or structure.
Reduces overlap: By placing DocDD and GSL under "Knowledge Interfaces," it’s clearer that one is philosophy (DocDD) and one is implementation (Structured Docs).
Clarifies flow: It now maps cleanly to how a real team moves from idea to delivery.
Is self-documenting: Each stage in the development flow produces artifacts that naturally explain the next. Documents define the behavior we test. Tests shape the models we build. Models inform the code we write. There’s no need to reconstruct intent after the fact—it’s already embedded in the process.