Model alignment
Model behavior, policy adherence, intent following, safety training, and behavioral evaluation.
Will the model behave as intended?
AI Alignment
Alignment can't prove the work was done.
AI labs work to align models with human intent. FalseGreen independently verifies whether autonomous software work actually satisfies the requirements humans defined.
Important distinction
FalseGreen does not align the model.
FalseGreen operates downstream of model alignment. It does not determine whether a model is benevolent, deceptive, safe, properly trained, or acting with good intentions.
It independently evaluates whether software produced by an autonomous engineering process satisfies a frozen definition of done. FalseGreen verifies the work, not the model's internal intent.
The operational boundary
Intent becomes software. Software still needs acceptance.
Alignment addresses behavior upstream. FalseGreen sits downstream, at the point where autonomous work must become a trusted result.
01
Human intent
People define the outcome and constraints.
02
Aligned model / agent
A model follows instructions and takes action.
03
Software work
The agent changes a real repository or system.
04
FalseGreen
Independent acceptance checks the frozen contract.
05
Result
Accepted · Failed · Insufficient Evidence
Alignment tells the AI coding agent what you want. Verification tells you whether you got it.
Authority separation
The builder should not be the final judge.
The same agent that generated the work should not be the final authority on whether that work satisfies the contract.
This is not an accusation of malicious behavior. It is separation of responsibilities: the builder can claim completion, while an independent acceptance layer checks what is actually true.
The agent never gets the final say.
From intent to acceptance
Make alignment enforceable at the software boundary.
1. Define
Human requirements become a frozen target.
2. Freeze
The definition of done is fixed before judgment.
3. Build
A human or existing agent does the work.
4. Verify
FalseGreen gathers evidence from the exact resulting source.
5. Decide
The result is Accepted, Failed, or Insufficient Evidence.
Good intent is not evidence
Alignment failures do not require malicious intent.
An AI coding agent can be helpful, follow its policy, and still produce work that misses the human-defined target.
- Specification misunderstanding
- Proxy optimization
- Incomplete implementation
- Test manipulation
- Stale assumptions
- Missing evidence
- Environmental mismatch
- Scope drift
FalseGreen does not need to know why the AI coding agent was wrong. It needs to determine whether the work satisfies the contract.
Complementary layers
Different questions. One responsible workflow.
FalseGreen
A specific engineering task, frozen requirements, execution evidence, software state, and independent acceptance.
Did this work satisfy the requirements?
Model alignment and independent software acceptance are complementary, not competing systems.
As autonomy grows
The more autonomous the worker, the more independent acceptance matters.
Keep the acceptance boundary outside the worker. FalseGreen binds the result to the exact source, frozen requirements, and evidence that earned it.
Software
Verify the implementation against the frozen requirements.
Evidence
Combine software, formal, security, and UI evidence.
Authority
Return Accepted, Failed, or Insufficient Evidence.