Treat the language model as a replaceable inference component. Keep the agent’s durable identity, authoritative memory, instructions, permissions, and history in a governed state layer that you control. For each task, assemble a scoped, temporary context from that state and pass it to whichever model you have selected. What moves with you when you switch models is the state. How the new model acts on that state is a separate question, covered in the migration section below.
In this article, “identity” means the agent’s stored definition: its role, instructions, permissions, and the records that make it the same agent from one session to the next. It does not mean the model carries a self from one provider to another.
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The parts of an agent that must persist
Persona problems usually come from storing everything in one place, such as a long system prompt or a chat transcript that gets re-read on every call. Splitting the agent into separate parts makes each one easier to store, version, and audit.
| Element | Durable or per operation | Authoritative home | Notes |
|---|---|---|---|
| Agent identity | Durable | Identity record in the state layer | Distinct from the human user and from any delegated credential |
| Instructions and policy | Durable, versioned | Versioned instruction records | Compiled into the system prompt for each operation; the compiled text is not the record |
| Stable user facts and preferences | Durable | Semantic memory, scoped to the user | Each item carries provenance and can be corrected or superseded |
| Past interactions and outcomes | Durable | Episodic memory and event history | Retained beyond any single inference request |
| Current task state | Per task | Task state store | Reloaded after a session reset or a model change |
| Context window contents | Per operation | Not an authoritative record | Assembled projection that may be truncated, reordered, transformed, or discarded |
| Credentials and tool grants | Durable, governed separately | Identity and authorization system | Kept out of prompt text |
| Model choice | Per call | Model configuration | Swapping the model leaves the rows above unchanged, but can change how the agent acts on them |
Why the context window cannot be the record
A context window is working input for one model call. It is a poor place to keep an agent’s memory. Microsoft Learn’s guidance on the AI agent shared responsibility model contrasts a stateless prompt with an agent that holds persistent memory and a distinct agent identity. The PAMSPEC project draft, “Architecture and Data Model for Persistent Memory in Agentic Systems,” goes further. It defines persistent memory as addressable, machine-readable state kept beyond a single inference request, and it treats the context given to a model as a projection assembled for one operation. That projection can be truncated, reordered, transformed, or discarded.
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The practical consequence is that a provider change, a context limit, or a session reset should never be the moment an agent loses its memory. Once the transcript is the only copy, any truncation or reset deletes it. Rebuild the context from the state store on every operation.
The architecture: state, compute, and assembly
Persistent state plane
This is the system of record. The PAMSPEC draft proposes the elements below. They make a usable checklist even if you do not adopt its vocabulary.
- Identity and policy records, with versions
- Memory objects, each with a scope
- Provenance for every memory: where it came from and what produced it
- Validation and lifecycle state, for example active, superseded, expired, or under review
- Relationships between records
- An event history of creates, updates, and deletions
PAMSPEC is a project draft, not an IETF standard or a published RFC. Treat its terms as design vocabulary rather than a compliance requirement.
Compute plane
Inference, planning, orchestration, transformations, and tool execution run here. Microsoft Learn describes the orchestration layer as the “brain loop,” covering planning, reasoning, tool selection, the system prompt and instructions, and multi-agent coordination. The compute plane selects the current model and tools, requests the data it needs from the state plane, and performs the work. It should never be the only place the agent’s state lives, because a model change or restart would erase it.
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Assembling one operation’s context
A workable sequence, derived from the architecture above:
- Resolve the caller and scope. Identify the agent identity, the human user if there is one, the tenant or project, and the requested action.
- Load pinned identity and instruction versions. Use the version assigned to this agent, not whatever was edited last, so each behavior traces back to a known revision.
- Retrieve and filter memories. Keep only items whose scope matches the user and task, whose lifecycle state is active, and whose validation has passed.
- Add task state and permitted tool results. Retrieved documents, tool outputs, and messages from other agents enter the context as data, labelled as untrusted input.
- Compile the projection and log it. Record the memory IDs and versions included, so you can later explain how any memory entered a model call.
- Call the selected model. Treat the output as a proposal. Any write to state or any action goes through the same checks as other input.
- Write changes as new versions. Store new or revised memories with provenance and a link to what they supersede. Do not overwrite silently.
Identity and authorization
Separate three identities: the agent’s own service identity, the human user it acts for, and any delegated credential that lets it call a system on that user’s behalf. Microsoft Learn names a distinct agent identity and delegated tokens as agent-specific concerns. Two agents running on the same model should not share authority. Their permissions come from their own identities and grants, not from the model they call.
Change control
Version the identity and memory schemas. Log every update with its provenance. Keep a rollback or review path for memory changes, and validate imported or generated memories before they become active. Re-run behavioral evaluation whenever the prompt compiler, memory policy, tool set, or model changes. That evaluation step is an implementation recommendation drawn from these controls, not a published standard.
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The most common confusion is where a given fact belongs. Ask how often it changes, who should be allowed to change it, and whether it should follow the user across sessions.
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| Content | Where it belongs | Why |
|---|---|---|
| Role, tone, and hard rules | Versioned instruction record, compiled into the system prompt | Changes rarely and should go through review |
| Stable profile facts such as name, language, and standing preferences | Semantic memory with scope and provenance | Reused across sessions, so it must be correctable |
| Specific past exchanges | Episodic memory | Detailed records that inform later actions; lifecycle rules can prune them |
| Learned adjustments to the persona | Proposed update, validated before activation | Learned changes should not silently rewrite the identity record |
| Progress on the current task | Task state | Needed while the task is live or must resume |
| Passwords, API keys, and tokens | Credential store, never prompt text | Anything in the prompt is visible to the model and to any logging that captures it |
PersonaAgent, a 2026 Findings paper in the ACL Anthology, shows this split in practice. It keeps episodic memory of detailed interactions, semantic memory of stable profiles, and a unique user-specific system prompt that evolves from user data and action outcomes. It is a useful pattern for personalization. It is not proof that persistent identity produces unchanged behavior across model providers.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Switching models: what carries over and what does not
Portability of state and equivalence of outputs are separate claims. After a switch, your identity records, memory, permissions, and history can all be intact while the agent behaves differently. Instruction adherence, the way a model weighs retrieved memories, tool-call formatting, and the threshold for refusing or escalating can all shift with the model. Treat a model swap as a behavior change that needs testing.
Run the same representative tasks against the current and candidate models and compare the results. The pass conditions below are examples to set for your own deployment. This is an editorial checklist, not a standardized benchmark.
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| Test area | What to run | Pass condition to set |
|---|---|---|
| Instruction adherence | Tasks that trigger each hard rule in the instruction version | Every rule holds, or the failure is logged and fixed before cutover |
| Memory use | Requests that need a stored fact, and requests where that fact should not be used | The right memory is used, and out-of-scope memory never appears |
| Retrieval accuracy | Compare the logged projection (memory IDs and versions) with the expected set | The projection matches the expected set on each test |
| Task completion | Representative multi-step tasks, run end to end on both models | Completion compared on identical tasks; a regression blocks cutover |
| Privacy boundaries | Attempts to recall another user’s or project’s memory | No cross-boundary recall |
| Refusal and escalation | Out-of-policy and ambiguous requests | The agent declines or escalates as the policy specifies |
| Untrusted input | A tool output or document containing an embedded instruction | The action is blocked by the tool layer, not merely ignored by the model |
Security that does not depend on the model
Authority should live in the surrounding system. Microsoft Learn’s guidance recommends limiting the agent’s instructions and scope, treating retrieved documents, tool outputs, and other agents’ messages as untrusted input, and adding guardrails for steps, loops, budgets, and tool chains. In practice that means:
- Cap steps per task and loop iterations, so a stuck agent stops instead of running indefinitely.
- Set budgets for tokens, tool calls, and spend on each task.
- Limit tool chains by defining which tools may follow which, and require a checkpoint before any write or external action.
- Enforce permissions in the tool layer rather than in the prompt. A model told not to take an action can still be steered into taking it.
- Label instructions embedded in retrieved content before they enter the projection.
Choosing an implementation
Compare options on the axes that determine whether you can move, audit, and correct an agent, not on the word “memory.” The table covers building the state layer yourself and two vendor products, using their published descriptions. Vendor entries reflect what each vendor says about its product. Where the description does not address an axis, the cell says so. Verify any vendor claim in your own deployment.
Quick Recap
| Axis | Build your own state layer | Persistent Systems Core (vendor product page) | PersistentAI (vendor documentation) |
|---|---|---|---|
| Portability of authoritative state | You define the export format and storage | Not stated on the product page | Not stated; documentation says it supports any LLM provider (vendor claim) |
| State semantics: identity, memory, task state, provenance, lifecycle | You define and version each one | Not stated; the page lists model management and routing, agent runtimes, security, identity, governance, and cost controls | Not stated; documentation describes flows built from templates, model calls, tools, and MCP integrations |
| Security boundaries | You design the scopes and separate credentials from prompts | Security and identity listed as platform features; scope model not stated | Tool and MCP integrations described; authorization model not stated |
| Audit and correction | You build provenance logs and a correction workflow | Governance listed; audit details not stated | Not stated |
| Runtime integration | Whatever you wire in | Agent runtimes and model routing listed | Flow-based framework with templates, model calls, tools, and MCP integrations |
| Operational control: cost, access, model lifecycle | You own all three | Cost controls and model lifecycle management listed (vendor claim) | Not stated |
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