diff --git a/server/ftchat/services/agent-engine.js b/server/ftchat/services/agent-engine.js
new file mode 100644
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+++ b/server/ftchat/services/agent-engine.js
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+/**
+ * Agent engine: context builder + toolchain orchestrator
+ * 
+ * Builds the message context for vLLM inference:
+ * - No system prompt (model trained on real conversations)
+ * - Previous conversation memory (mother tongue imprint)
+ * - Tool results (Notion, repo) injected as user/assistant messages
+ * - Auto conversation rotation at threshold
+ */
+'use strict';
+
+const vllm = require('./vllm-proxy');
+const sessionStore = require('./session-store');
+const memoryAgent = require('./memory-agent');
+const toolRegistry = require('./tool-registry');
+
+const MAX_MESSAGES_BEFORE_ROTATE = 50;
+const MAX_CONTEXT_TOKENS = 3072;
+
+/**
+ * Build the full message context for vLLM
+ * 
+ * Context structure:
+ * [
+ *   {role:'user', content:'[Mother Tongue Imprint from last session]'},
+ *   {role:'assistant', content:'[Understood. Continuing from where we left off.]'},
+ *   ...previous messages,
+ *   {role:'user', content:'current message'}
+ * ]
+ * 
+ * NO system prompt - the model was trained on real conversations without system prompts.
+ */
+function buildContext(userHash, slotIndex, newMessage, conversationHistory, options = {}) {
+  const messages = [];
+
+  // 1. Cross-session memory imprint (if exists)
+  const imprint = sessionStore.getLatestImprint(userHash);
+  if (imprint) {
+    messages.push({
+      role: 'user',
+      content: '[Previous conversation memory]\n' + imprint
+    });
+    messages.push({
+      role: 'assistant',
+      content: '[I understand. I will continue based on our previous conversation.]'
+    });
+  }
+
+  // 2. Current conversation history
+  const history = conversationHistory || [];
+  for (const msg of history) {
+    if (msg.role === 'system') continue; // Never inject system messages
+    messages.push({
+      role: msg.role,
+      content: msg.content || ''
+    });
+  }
+
+  // 3. Current user message
+  messages.push({
+    role: 'user',
+    content: newMessage || ''
+  });
+
+  return messages;
+}
+
+/**
+ * Process a chat message through the agent pipeline:
+ * 1. Build context (memory + history)
+ * 2. Send to vLLM
+ * 3. Check if model requested a tool call
+ * 4. If tool call: execute tool, inject result, re-request
+ * 5. Return final response
+ */
+async function processMessage(userHash, slotIndex, message, conversationHistory = []) {
+  const context = buildContext(userHash, slotIndex, message, conversationHistory);
+
+  // Send to vLLM
+  let response = await vllm.chatOnce(context, {
+    maxTokens: 1024,
+    temperature: 0.7
+  });
+
+  // Check for tool calls
+  const toolCall = toolRegistry.parseToolCall(response);
+  if (toolCall) {
+    context.push({ role: 'assistant', content: response });
+
+    try {
+      const toolResult = await toolRegistry.executeToolCall(toolCall);
+      context.push({ role: 'user', content: '[Tool Result]\n' + JSON.stringify(toolResult, null, 2) });
+
+      // Re-request with tool result
+      response = await vllm.chatOnce(context, {
+        maxTokens: 1024,
+        temperature: 0.7
+      });
+    } catch (err) {
+      context.push({ role: 'user', content: '[Tool Error]\n' + err.message });
+      response = await vllm.chatOnce(context, {
+        maxTokens: 512,
+        temperature: 0.7
+      });
+    }
+  }
+
+  return {
+    response,
+    shouldRotate: conversationHistory.length >= MAX_MESSAGES_BEFORE_ROTATE
+  };
+}
+
+/**
+ * Stream a chat message through the pipeline, writing SSE to res
+ */
+async function streamProcessMessage(userHash, slotIndex, message, conversationHistory, res) {
+  const context = buildContext(userHash, slotIndex, message, conversationHistory);
+
+  // Stream from vLLM
+  vllm.pipeChat(context, res, {
+    maxTokens: 1024,
+    temperature: 0.7
+  });
+
+  return {
+    shouldRotate: conversationHistory.length >= MAX_MESSAGES_BEFORE_ROTATE
+  };
+}
+
+function getMaxMessages() {
+  return MAX_MESSAGES_BEFORE_ROTATE;
+}
+
+module.exports = {
+  buildContext,
+  processMessage,
+  streamProcessMessage,
+  getMaxMessages
+};