Learn how UI, session, and data state move between frontend and backend in AI apps, with practical patterns for syncing, persistence, caching, and security.

“State” is everything your app needs to remember in order to behave correctly from one moment to the next.
If a user clicks Send in a chat UI, the app shouldn’t forget what they typed, what the assistant already replied, whether a request is still running, or what settings (tone, model, tools) are enabled. All of that is state.
A useful way to think about state is: the current truth of the app—values that affect what the user sees and what the system does next. That includes obvious things like form inputs, but also “invisible” facts like:
Traditional apps often read data, show it, and save updates. AI apps add extra steps and intermediate outputs:
That extra motion is why state management is often the hidden complexity in AI applications.
In the sections ahead, we’ll break state into practical categories (UI state, session state, persisted data, and model/runtime state), and show where each should live (frontend vs. backend). We’ll also cover syncing, caching, long-running jobs, streaming updates, and security—because state is only helpful if it’s correct and protected.
Imagine a chat app where a user asks: “Summarize last month’s invoices and flag anything unusual.” The backend might (1) fetch invoices, (2) run an analysis tool, (3) stream a summary back to the UI, and (4) save the final report.
For that to feel seamless, the app must keep track of messages, tool results, progress, and the saved output—without mixing up conversations or leaking data between users.
When people say “state” in an AI app, they often mix together very different things. Splitting state into four layers—UI, session, data, and model/runtime—makes it easier to decide where something should live, who can change it, and how it should be stored.
UI state is the live, moment-to-moment state in the browser or mobile app: text inputs, toggles, selected items, which tab is open, and whether a button is disabled.
AI apps add a few UI-specific details:
UI state should be easy to reset and safe to lose. If the user refreshes the page, you may lose it—and that’s usually fine.
Session state ties a user to an ongoing interaction: user identity, a conversation ID, and a consistent view of message history.
In AI apps, this often includes:
This layer often spans frontend and backend: the frontend holds lightweight identifiers, while the backend is the authority for session continuity and access control.
Data state is what you store intentionally in a database: projects, documents, embeddings, preferences, audit logs, billing events, and saved conversation transcripts.
Unlike UI and session state, data state should be:
Model/runtime state is the operational setup used to produce an answer: system prompts, tools enabled, temperature/max tokens, safety settings, rate limits, and temporary caches.
Some of it is configuration (stable defaults); some is ephemeral (short-lived caches or per-request token budgets). Most of it belongs on the backend so it can be controlled consistently and not exposed unnecessarily.
When these layers blur, you get classic failures: the UI shows text that wasn’t saved, the backend uses different prompt settings than the frontend expects, or conversation memory “leaks” between users. Clear boundaries create clearer sources of truth—and make it obvious what must persist, what can be recomputed, and what must be protected.
A reliable way to reduce bugs in AI apps is to decide, for every piece of state, where it should live: in the browser (frontend), on the server (backend), or in both. This choice affects reliability, security, and how “surprising” the app feels when users refresh, open a new tab, or lose network connection.
Frontend state is best for things that change quickly and don’t need to survive a refresh. Keeping it local makes the UI responsive and avoids unnecessary API calls.
Common frontend-only examples:
If you lose this state on refresh, it’s usually acceptable (and often expected).
Backend state should hold anything that must be trusted, audited, or consistently enforced. This includes state that other devices/tabs need to see, or that must remain correct even if the client is modified.
Common backend-only examples:
A good mindset: if incorrect state could cost money, leak data, or break access control, it belongs on the backend.
Some state is naturally shared:
Even when shared, pick a “source of truth.” Typically, the backend is authoritative and the frontend caches a copy for speed.
Keep state closest to where it’s needed, but persist what must survive refresh, device changes, or interruptions.
Avoid the anti-pattern of storing sensitive or authoritative state only in the browser (for example, treating a client-side isAdmin flag, plan tier, or job completion state as truth). The UI can display these values, but the backend must verify them.
An AI feature feels like “one action,” but it’s really a chain of state transitions shared between the browser and the server. Understanding the lifecycle makes it easier to avoid mismatched UI, missing context, and duplicated charges.
A user clicks Send. The UI immediately updates local state: it may add a “pending” message bubble, disable the send button, and capture current inputs (text, attachments, selected tools).
At this point the frontend should generate or attach correlation identifiers:
These IDs let both sides talk about the same event even when responses arrive late or twice.
The frontend sends an API request with the user message plus the IDs. The server validates permissions, rate limits, and payload shape, then persists the user message (or at least an immutable log record) keyed by conversation_id and message_id.
This persistence step prevents “phantom history” when the user refreshes mid-request.
To call the model, the server rebuilds context from its source of truth:
The key idea: don’t rely on the client to provide the full history. The client can be stale.
The server may call tools (search, database lookup) before or during model generation. Each tool call produces intermediate state that should be tracked against the request_id so it can be audited and retried safely.
With streaming, the server sends partial tokens/events. The UI incrementally updates the pending assistant message, but still treats it as “in progress” until a final event marks completion.
Retries, double-submits, and out-of-order responses happen. Use request_id to dedupe on the server, and message_id to reconcile in the UI (ignore late chunks that don’t match the active request). Always show a clear “failed” state with a safe retry that does not create duplicate messages.
A session is the “thread” that ties a user’s actions together: which workspace they’re in, what they last searched for, which draft they were editing, and which conversation an AI reply should continue. Good session state makes the app feel continuous across pages—and ideally across devices—without turning your backend into a dumping ground for everything the user ever said.
Aim for: (1) continuity (a user can leave and come back), (2) correctness (the AI uses the right context for the right conversation), and (3) containment (one session can’t leak into another). If you support multiple devices, treat sessions as user-scoped plus device-scoped: “same account” doesn’t always mean “same open work.”
You’ll usually pick one of these ways to identify the session:
HttpOnly, Secure, SameSite) and handle CSRF appropriately.“Memory” is just state you choose to send back into the model.
A practical pattern is summary + window: it’s predictable and helps avoid surprising model behavior.
If the AI uses tools (search, database queries, file reads), store each tool call with: inputs, timestamps, tool version, and the returned output (or a reference to it). This lets you explain “why the AI said that,” replay runs for debugging, and detect when results changed because a tool or dataset changed.
Don’t store long-lived memory by default. Keep only what you need for continuity (conversation IDs, summaries, and tool logs), set retention limits, and avoid persisting raw user text unless there’s a clear product reason and user consent.
State gets risky when the same “thing” can be edited in more than one place—your UI, a second browser tab, or a background job updating a conversation. The fix is less about clever code and more about clear ownership.
Decide which system is authoritative for each piece of state. In most AI applications, the backend should own the canonical record for anything that must be correct: conversation settings, tool permissions, message history, billing limits, and job status. The frontend can cache and derive state for speed (selected tab, draft prompt text, “is typing” indicators), but it should assume the backend is right when there’s a mismatch.
A practical rule: if you’d be upset losing it on refresh, it probably belongs in the backend.
Optimistic updates make the app feel instant: toggle a setting, update the UI immediately, then confirm with the server. This works well for low-stakes, reversible actions (e.g., starring a conversation).
It causes confusion when the server might reject or transform the change (permission checks, quota limits, validation, or server-side defaults). In those cases, show a “saving…” state and update the UI only after confirmation.
Conflicts happen when two clients update the same record based on different starting versions. Common example: Tab A and Tab B both change the model temperature.
Use lightweight versioning so the backend can detect stale writes:
updated_at timestamps (simple, human-debuggable)If-Match headers (HTTP-native)If the version doesn’t match, return a conflict response (often HTTP 409) and send back the latest server object.
After any write, have the API return the saved object as persisted (including server-generated defaults, normalized fields, and the new version). This lets the frontend replace its cached copy immediately—one source-of-truth update instead of guessing what changed.
Caching is one of the quickest ways to make an AI app feel instant, but it also creates a second copy of state. If you cache the wrong thing—or cache it in the wrong place—you’ll ship a UI that feels fast and confusing at the same time.
Client-side caches should focus on experience, not authority. Good candidates include recent conversation previews (titles, last message snippet), UI preferences (theme, selected model, sidebar state), and optimistic UI state (messages that are “sending”).
Keep the client cache small and disposable: if it’s cleared, the app should still work by refetching from the server.
Server caches should focus on expensive or frequently repeated work:
This is also where you can cache derived state such as token counts, moderation decisions, or document parsing outputs—anything deterministic and costly.
Three practical rules:
user_id, model, tool parameters, document version).If you can’t explain when a cache entry becomes wrong, don’t cache it.
Avoid putting API keys, auth tokens, raw prompts containing sensitive text, or user-specific content into shared layers like CDN caches. If you must cache user data, isolate by user and encrypt at rest—or keep it in your primary database instead.
Caching should be proven, not assumed. Track p95 latency before/after, cache hit rate, and user-visible errors like “message updated after rendering.” A fast response that later contradicts the UI is often worse than a slightly slower, consistent one.
Some AI features finish in a second. Others take minutes: uploading and parsing a PDF, embedding and indexing a knowledge base, or running a multi-step tool workflow. For these, “state” isn’t just what’s on the screen—it’s what survives refreshes, retries, and time.
Persist only what unlocks real product value.
Conversation history is the obvious one: messages, timestamps, user identity, and (often) which model/tooling was used. This powers “resume later,” audit trails, and better support.
User and workspace settings should live in the database: preferred model, temperature defaults, feature toggles, system prompts, and UI preferences that should follow the user across devices.
Files and artifacts (uploads, extracted text, generated reports) are usually stored in object storage with database records pointing to them. The database holds metadata (owner, size, content type, processing state), while the blob store holds the bytes.
If a request can’t reliably finish within a normal HTTP timeout, move the work to a queue.
A typical pattern:
POST /jobs with inputs (file id, conversation id, parameters).job_id.This keeps the UI responsive and makes retries safer.
Make job state explicit and queryable: queued → running → succeeded/failed (optionally canceled). Store these transitions server-side with timestamps and error details.
On the frontend, reflect status clearly:
Expose GET /jobs/{id} (polling) or stream updates (SSE/WebSocket) so the UI never has to guess.
Network timeouts happen. If the frontend retries POST /jobs, you don’t want two identical jobs (and two bills).
Require an Idempotency-Key per logical action. The backend stores the key with the resulting job_id/response and returns the same result for repeated requests.
Long-running AI apps accumulate data fast. Define retention rules early:
Treat cleanup as part of state management: it reduces risk, cost, and confusion.
Streaming makes state trickier because the “answer” isn’t a single blob anymore. You’re dealing with partial tokens (text arriving word by word) and sometimes partial tool work (a search starts, then finishes later). That means your UI and your backend must agree on what counts as temporary vs. final state.
A clean pattern is to stream a sequence of small events, each with a type and a payload. For example:
token: incremental text (or a small chunk)tool_start: a tool call began (e.g., “Searching…”, with an id)tool_result: tool output is ready (same id)done: the assistant message is completeerror: something failed (include a user-safe message and a debug id)This event stream is easier to version and debug than raw text streaming, because the frontend can render progress accurately (and show tool status) without guessing.
On the client, treat streaming as append-only: create a “draft” assistant message and keep extending it as token events arrive. When you receive done, perform a commit: mark the message final, persist it (if you store locally), and unlock actions like copy, rate, or regenerate.
This avoids rewriting history mid-stream and keeps your UI predictable.
Streaming increases the chance of half-finished work:
If the page reloads mid-stream, reconstruct from the latest stable state: the last committed messages plus any stored draft metadata (message id, accumulated text so far, tool statuses). If you can’t resume the stream, show the draft as interrupted and let the user retry, rather than pretending it completed.
State is not just “data you store”—it’s the user’s prompts, uploads, preferences, generated outputs, and the metadata that ties everything together. In AI apps, that state can be unusually sensitive (personal info, proprietary docs, internal decisions), so security needs to be designed into each layer.
Anything that would let a client impersonate your app must stay backend-only: API keys, private connectors (Slack/Drive/DB credentials), and internal system prompts or routing logic. The frontend can request an action (“summarize this file”), but the backend should decide how it’s executed and with which credentials.
Treat each state mutation as a privileged operation. When the client tries to create a message, rename a conversation, or attach a file, the backend should verify:
This prevents “ID guessing” attacks where someone swaps a conversation_id and accesses another user’s history.
Assume any client-provided state is untrusted input. Validate schema and constraints (types, lengths, allowed enums), and sanitize for the destination (SQL/NoSQL, logs, HTML rendering). If you accept “state updates” (e.g., settings, tool parameters), whitelist allowed fields rather than merging arbitrary JSON.
For actions that change durable state—sharing, exporting, deleting, connector access—record who did what and when. A lightweight audit log helps with incident response, customer support, and compliance.
Store only what you need to deliver the feature. If you don’t need full prompts forever, consider retention windows or redaction. Encrypt sensitive state at rest where appropriate (tokens, connector creds, uploaded documents) and use TLS in transit. Separate operational metadata from content so you can restrict access more tightly.
A useful default for AI apps is simple: the backend is the source of truth, and the frontend is a fast, optimistic cache. The UI can feel instant, but anything you’d be sad to lose (messages, job status, tool outputs, billing-relevant events) should be confirmed and stored server-side.
If you’re building with a “vibe-coding” workflow—where a lot of product surface area gets generated quickly—the state model becomes even more important. Platforms like Koder.ai can help teams ship full web, backend, and mobile apps from chat, but the same rule still holds: rapid iteration is safest when your sources of truth, IDs, and status transitions are designed up front.
Frontend (browser/mobile)
session_id, conversation_id, and a new request_id.Backend (API + workers)
Note: one practical way to keep this consistent is to standardize your backend stack early. For example, Koder.ai-generated backends commonly use Go with PostgreSQL (and React on the frontend), which makes it straightforward to centralize “authoritative” state in SQL while keeping the client cache disposable.
Before building screens, define the fields you will rely on in every layer:
user_id, org_id, conversation_id, message_id, request_id.created_at, updated_at, and an explicit sequence for messages.queued | running | streaming | succeeded | failed | canceled (for jobs and tool calls).etag or version for conflict-safe updates.This prevents the classic bug where the UI “looks right” but can’t reconcile retries, refreshes, or concurrent edits.
Keep endpoints predictable across features:
GET /conversations (list)GET /conversations/{id} (get)POST /conversations (create)POST /conversations/{id}/messages (append)PATCH /jobs/{id} (update status)GET /streams/{request_id} or POST .../stream (stream)Return the same envelope style everywhere (including errors) so the frontend can update state uniformly.
Log and return a request_id for every AI call. Record tool-call inputs/outputs (with redaction), latency, retries, and final status. Make it easy to answer: “What did the model see, what tools ran, and what state did we persist?”
request_id (and/or an Idempotency-Key).queued to succeeded).version/etag or server-side merge rules.When you adopt faster build cycles (including AI-assisted generation), consider adding guardrails that enforce these checklist items automatically—schema validation, idempotency, and evented streaming—so “moving fast” doesn’t turn into state drift. In practice, that’s where an end-to-end platform like Koder.ai can be useful: it speeds up delivery, while still allowing you to export source code and keep state-handling patterns consistent across web, backend, and mobile builds.