Objective

Before an agent can act, it has to know what the user wants: is this a billing question, a bug report, a sales lead, or a cancellation? Brittle keyword rules break on paraphrase, and spinning up a classifier service is overkill. Here you'll classify intent by meaning: store a few labeled examples per intent, embed them, and match an incoming message to the nearest examples — a k-NN intent classifier that needs no training job and adds new intents by inserting rows. The same classifier works from any framework or a voice agent — see Use it from your agent at the end.

Step 1: Create the labeled-examples store

Each row is an example utterance tagged with its intent label and embedded for matching.

CREATE TABLE IF NOT EXISTS recipe_intent_examples (
  example_id INTEGER PRIMARY KEY,
  intent     TEXT,                                   -- the label this example teaches
  utterance  TEXT,                                   -- a sample way users phrase that intent
  embedding  VECTOR(384)
);

Step 2: Seed a few examples per intent

A handful of phrasings per intent is enough — the embedding model generalizes to unseen wording.

INSERT INTO recipe_intent_examples (example_id, intent, utterance) VALUES
 (1,'billing','Why was I charged twice this month?'),
 (2,'billing','I need a copy of my last invoice.'),
 (3,'billing','How do I update my credit card?'),
 (4,'tech_support','The app crashes when I open the dashboard.'),
 (5,'tech_support','I keep getting a 500 error on upload.'),
 (6,'tech_support','The export button does nothing.'),
 (7,'sales','Do you offer a plan for large teams?'),
 (8,'sales','Can I get a demo before buying?'),
 (9,'sales','What''s included in the enterprise tier?'),
 (10,'cancel','I want to close my account.'),
 (11,'cancel','How do I stop my subscription?'),
 (12,'cancel','Please cancel my plan and refund me.');

Step 3: Embed the examples

The embedding model runs in-database; this is the labeled index the classifier searches.

UPDATE recipe_intent_examples SET embedding = EMBED(utterance);

Step 4: Classify a new message by nearest examples

Find the closest labeled examples to the incoming message — their intent is the prediction.

SELECT intent, utterance,
       COSINE_SIMILARITY(embedding, EMBED('my card got declined, can you help me pay?')) AS similarity
FROM recipe_intent_examples
ORDER BY similarity DESC
LIMIT 3;

Step 5: Vote across the top-k for a robust label

Average similarity per intent over the nearest examples — majority-by-meaning beats a single match.

SELECT intent, AVG(similarity) AS confidence, COUNT(*) AS votes
FROM (
  SELECT intent,
         COSINE_SIMILARITY(embedding, EMBED('the page keeps throwing an error when I click save')) AS similarity
  FROM recipe_intent_examples
  ORDER BY similarity DESC
  LIMIT 5
)
GROUP BY intent
ORDER BY confidence DESC
LIMIT 1;

Step 6: Gate low-confidence intents to a fallback

Return the predicted intent only when it clears a threshold; otherwise the agent should ask, not assume.

SELECT intent, confidence,
       CASE WHEN confidence >= 0.30 THEN intent ELSE 'unknown_ask_clarify' END AS routed_intent
FROM (
  SELECT intent, AVG(similarity) AS confidence
  FROM (
    SELECT intent,
           COSINE_SIMILARITY(embedding, EMBED('hi there, hope you are well')) AS similarity
    FROM recipe_intent_examples
    ORDER BY similarity DESC
    LIMIT 5
  )
  GROUP BY intent
  ORDER BY confidence DESC
  LIMIT 1
);

Cleanup (Optional)

DROP TABLE IF EXISTS recipe_intent_examples;

Expected Outcomes

Step 4 classifies "my card got declined" as billing, matching examples with no shared keywords.
Step 5 votes "the page throws an error on save" to tech_support with a confidence and vote count.
Step 6 routes a content-free greeting to unknown_ask_clarify because no intent clears the gate — the agent asks instead of guessing.

You now have a k-NN intent classifier that needs no training run, adds intents by inserting rows, and routes confidently or asks when unsure.

Use it from your agent (framework-agnostic — this is the whole point)

Intent classification is just labeled examples + a nearest-neighbor query, so any agent uses it with no framework lock-in:

REST / SDK — POST /v1/query/execute (any language), or @synapcores/sdk client.executeQuery(...). On each incoming message your agent runs the Step-5/6 query, reads routed_intent, and branches its logic accordingly. Add or correct intents by inserting example rows — no retraining.
MCP (native, on by default) — point any MCP client (Claude Code, Cursor, a custom loop, a voice runtime) at ws://<your-instance>/mcp?token=<jwt> (JWT from one POST /v1/auth/login → access_token). The query tool returns the intent; the execute tool adds new labeled examples — classification and continuous improvement as tool calls.
Scale-up option — once you have many labeled rows, train a supervised classifier with CREATE EXPERIMENT … task_type='binary_classification' (one model per intent, or one-vs-rest) and serve it with AUTOML.PREDICT. The vector classifier here needs zero training and is ideal to start.
Any framework — OpenClaw, LangChain routers, a custom loop, or a voice agent all classify against the same examples table. The database is the brain; the framework is swappable.

Key Concepts Learned

Embedding labeled examples turns intent classification into nearest-neighbor search — no training job, robust to paraphrase.
Voting across the top-k (averaging similarity per intent) gives a stable label and a confidence.
A confidence gate routes ambiguous messages to clarification instead of a wrong branch.
Because it's plain data ops (SQL / REST / MCP), intent classification works for any agent — the agent-agnostic backend pattern this cluster builds on.

Intent Classification for an Agent