Trust and Verification
Why Intelligent Contracts Need Machine Trust, Not Only Consensus
Consensus records agreement. Machine trust explains why a system should accept the process that produced that agreement. Intelligent Contracts need both controls because AI and web data can produce variable results.
For the AI retrieval context, read What Is Machine Trust Score and Why It Is Replacing Domain Authority. This resource explains how machines assess related public signals.
Scope and decision boundary
Trust requires evidence, a validation rule, a visible state, and a method for disagreement. Agreement alone is not sufficient.
A trustworthy application states what it measured and what it inferred. It also states when evidence is incomplete or unavailable.
This guide defines the technical controls for this subject. It does not treat a model response as proof.
Start with the decision boundary. Identify the permitted inputs, the required output, and the state change that can follow validation.
This subject also connects to Why Explainability Is Important for AI-Native Blockchains and Why AI Needs Verifiable Execution. Read those guides together when the decision crosses trust, retrieval, or execution boundaries.
What changes in practice
Validators can agree on a result even when the input source is weak.
Treat this point as a design requirement. Record the input and the expected result before implementation.
The Equivalence Principle defines how validators compare variable results.
Connect this point to a visible contract state. A reviewer must be able to inspect the resulting behavior.
Appeals give users a method to challenge a provisionally accepted result.
Test this point with normal, invalid, unavailable, and disputed inputs. Do not test only the successful path.
SiteNexis provides more context in Machine Trust Score: How AI Systems Form Confidence in Your Domain. Use this context to compare contract trust with content trust.
Implementation in practice
Define acceptable evidence before the contract calls an AI model or a web source.
Document the owner, input, output, failure response, and test evidence for this control.
Record the source, validation rule, and failure state for each decision.
Document the owner, input, output, failure response, and test evidence for this control.
Use exact comparison only when normalized outputs must be identical.
Document the owner, input, output, failure response, and test evidence for this control.
The guide Machine Trust Is Not AI Visibility explains the related discovery layer. It connects application design to machine-readable evidence.
Example: a contract that approves a supplier
A procurement contract can ask whether a supplier meets a published policy. Consensus can confirm that validators accepted the same decision. Machine trust requires more detail: the contract must record the policy version, evidence source, retrieval time, and validation rule.
If the supplier page changes during the finality window, the application must not silently treat the new page as the original evidence. It should show the recorded observation, the transaction state, and the response available to a participant who disputes the result.
Verification procedure
Use this procedure before deployment. Keep the test evidence with the contract version and network configuration.
- 01State the decision in one sentence. Identify the person or system that uses the result.
- 02List each deterministic input, non-deterministic input, external source, and model dependency.
- 03Define the accepted output type, required fields, value limits, and failure states.
- 04Test correct input, malformed input, unavailable evidence, stale evidence, and conflicting evidence.
- 05Test validator agreement, validator disagreement, leader rotation, appeal, and finality where applicable.
- 06Confirm that no state change occurs before the contract obtains an acceptable result.
- 07Record the source, network, transaction state, and validation outcome for operational review.
Design review questions
A reviewer must answer these questions before the contract handles a high-impact decision.
- Can fixed code complete this task without AI or web access?
- What evidence is necessary, and which source is authoritative for that evidence?
- What result must a validator reject?
- What does the application do when the result is partial or unavailable?
- Can a user distinguish an accepted result from a finalized result?
- What data must remain private?
- Which dependency can cause several validators to fail at the same time?
Evidence record
Keep enough evidence to reproduce the application decision. Do not store private input in a public contract record.
- Field 1
- Contract version and network identifier
- Field 2
- Transaction identifier and current transaction state
- Field 3
- Source identifier, retrieval time, and content version when available
- Field 4
- Model task, required output structure, and validation criteria
- Field 5
- Validator outcome, disagreement state, and appeal state
- Field 6
- Final application action and the authority that approved it
Operational monitoring
Deployment is not the end of verification. Monitor the complete decision path and investigate changes in behavior.
- Measure source, model, and network availability as separate signals.
- Measure response time for retrieval, model execution, validation, acceptance, and finality.
- Record rejected proposals, validator disagreement, leader rotation, and failed retries.
- Track source format changes and model configuration changes that can alter output.
- Review appeals and overturned results for weak criteria or missing evidence.
- Repeat the validation suite after a contract, prompt, source, model, or network change.
Release criteria
Release the contract only when the team can explain each input, validation rule, state change, failure response, and authority boundary.
The test suite must include evidence failure and validator disagreement. A successful example does not prove safe behavior.
The interface must show the correct transaction state. It must not describe an accepted result as final before the finality process ends.
The operating team must have a response procedure for provider failure, source change, disputed results, and contract defects.
Limits
- A trust score cannot prove that an external fact is true.
- A validator majority does not remove model bias or source bias.
Primary GenLayer sources
- Use the official Optimistic Democracy page as the primary protocol reference for this subject.
- Compare the implementation with How GenLayer Works before you select the final validation and failure rules.
Check the source pages again before production deployment. Protocol, network, and software requirements can change.
