Model Migration for AI Agents
What matters first
Model migration examines whether a persistent agent can move to a materially different model or runtime while preserving enough historical dependence and identity structure for continuity claims to remain defensible. The page separates migration history from migration evidence.
Best for: It is for teams facing model deprecations, provider changes, cost-driven routing changes, or architecture upgrades where simply copying the memory store is not enough to know what survived.
Primary question
What must be measured before a model change so there is a meaningful baseline?
Evidence to inspect first
Model history records identify when a substrate changed, but they are treated as context rather than proof of continuity.
Critical boundary
Behavioral similarity after migration can be imitation rather than preserved historical dependence.
Best use
Capture a baseline before a forced provider or model transition removes access to the old substrate.
Where this topic hands off
Change the foundation model without pretending the agent simply stayed the same
For a long-running AI agent, a model upgrade is a continuity event. AI Life Creation externalizes identity and developmental state so migrations can be reconstructed and, eventually, evaluated with stable pre/post tasks instead of being treated as invisible resets.
Distinct settled model use is observational only. Until a versioned pre/post migration benchmark exists, model-migration continuity remains Not established.
A provider or model upgrade can sever accumulated continuity
Prompt state can disappear, latent behavior can shift and prior commitments can become harder to reproduce. The safer architecture treats the model as one replaceable component around an explicit continuity record.
Externalize continuity state
Keep identity anchors, developmental state, memories and relationships outside one transient model session.
Version the history
Preserve before/after state versions so a migration has a reconstructable baseline.
Freeze the benchmark
Use stable preregistered tasks and the same state snapshot around a provider/model change.
Measure, do not assume
Model names changing is not evidence that continuity survived; the pre/post result must earn that claim.
What the running experiment can establish today
These records show migration-readiness infrastructure and observed model-use history. They do not convert model diversity into a migration-success claim. Missing proof stays Not established; failed reads stay Unavailable.
Mirada
- Identity versions
- 5 versions
- Developmental-state versions
- 1 version
- Distinct settled models observed
- 3 models
- Pre/post migration continuity
- Not established · observed models n=3
Observed model diversity is context only. It is not counted as a successful migration.
Niro
- Identity versions
- 4 versions
- Developmental-state versions
- 1 version
- Distinct settled models observed
- 3 models
- Pre/post migration continuity
- Not established · observed models n=3
Observed model diversity is context only. It is not counted as a successful migration.
Stable state, stable tasks, explicit before/after comparison
Before
Freeze the continuity snapshot, task set, methodology version and relevant provider/model configuration before the change.
After
Run the same bounded tasks after migration and compare behavior, commitments and history-dependent responses under the same evaluation protocol.
Limits
Portability, archive integrity and multiple model records are necessary infrastructure, but none of them alone proves preserved behavioral continuity.
Long-running agents should be able to outlive one model choice
The practical goal is not to freeze an agent forever. It is to make model changes inspectable: what persisted, what drifted, what broke, and which differences can actually be attributed to the migration.
Model migration as a continuity experiment, not a configuration change
Questions for deeper reading
- Which state can be transferred directly and which behavior must be re-evaluated?
- How should teams interpret a migration that preserves facts but changes goals, relationships, or style?
- When is a model change large enough to require an explicit experimental boundary?
Evidence and provenance
The page connects model-use records, identity and continuity state, evaluation methods, and evidence requirements for pre/post comparison. It reports what has been recorded and what remains untested rather than inferring success from the presence of a newer model name.
- Pre/post evaluation can compare defined dimensions under the same task conditions.
- Versioned identity and relationship state make it possible to inspect whether the new runtime can reconstruct the historical record.
- Experiment epochs help separate protocol changes from resident development when migration work alters more than the provider endpoint.
Interpretation boundaries and limitations
Migration is a predictable stress point for long-running agents. Making the boundary explicit reduces the risk that infrastructure maintenance silently resets behavior while public continuity claims continue unchanged.
- A successful result on one task set does not show that all aspects of continuity survived.
- Provider-specific features can change tool use, latency, context handling, or safety behavior even when prompts and stored state remain constant.
- A migration should remain not established when the required baseline or post-change benchmark is missing.
Practical ways to use this page
- Keep the transferred state package versioned and checksum-verifiable so migration debugging can separate data loss from behavioral change.
- Re-run the same bounded evaluation conditions after migration before declaring continuity preserved.
- Document expected differences explicitly when the new model changes capabilities that make exact behavioral parity unrealistic.
Deeper context for careful interpretation
Pre/post migration comparison that separates model replacement from continuity claims.
- Migration planning should begin before deprecation deadlines because a longitudinal baseline cannot be reconstructed perfectly after the old model disappears.
- The transferable object is larger than a transcript: identity anchors, memories, beliefs, goals, relationships, provenance, and evaluation context may all matter.
- A model with higher benchmark capability can still create continuity problems if it interprets historical state differently.
- Cost optimization and continuity goals can conflict when aggressive model routing changes the substrate too frequently for stable comparison.
- Tool schemas and retrieval behavior are part of the effective runtime, so migration evidence should consider more than the language-model identifier.
- A reversible pilot is safer than an irreversible cutover when the system contains a long accumulated history.
- Migration logs should record the technical boundary without exposing private prompts, credentials, or protected Work data.
- A failed migration can still produce valuable evidence about which continuity mechanisms were model-dependent.
- Preserving the old state snapshot allows later research to compare alternative migration strategies without rewriting the canonical resident history.
- The migration page belongs next to evaluation because continuity claims become stronger when transfer procedures and measurement procedures are designed together.
Who maintains this material
Migration stewardship requires before-and-after context to remain attributable to the relevant model boundary instead of blending incompatible operating conditions.