diff --git a/workers/site/services/ChatService.ts b/workers/site/services/ChatService.ts
index 742a689..eb7bd8c 100644
--- a/workers/site/services/ChatService.ts
+++ b/workers/site/services/ChatService.ts
@@ -14,7 +14,6 @@ import {XaiChatSdk} from "../sdk/models/xai";
 import {CerebrasSdk} from "../sdk/models/cerebras";
 import {CloudflareAISdk} from "../sdk/models/cloudflareAi";
 
-// Types
 export interface StreamParams {
   env: Env;
   openai: OpenAI;
@@ -92,10 +91,8 @@ const ChatService = types
       const handleAgentProcess = async (
           {controller, encoder, webhook, dynamicContext}: StreamHandlerParams
       ) => {
-        console.log("handleAgentProcess::start");
         if (!webhook) return;
-        console.log("handleAgentProcess::[Loading Live Search]");
-        dynamicContext.append("\n## Live Search\n~~~markdown\n");
+        dynamicContext.append("\n## Agent Results\n~~~markdown\n");
 
         for await (const chunk of self.streamAgentData({webhook})) {
           controller.enqueue(encoder.encode(chunk));
@@ -103,9 +100,7 @@ const ChatService = types
         }
 
         dynamicContext.append("\n~~~\n");
-        console.log(`handleAgentProcess::[Finished loading Live Search!][length: ${dynamicContext.content.length}]`);
         ChatSdk.sendDoubleNewline(controller, encoder);
-        console.log("handleAgentProcess::exit")
       };
 
       const createStreamParams = async (
@@ -331,12 +326,10 @@ const ChatService = types
               const encoder = new TextEncoder();
 
               try {
-                // Send initial retry directive
-                // controller.enqueue(encoder.encode('retry: 0\n\n'));
                 const dynamicContext = Message.create(streamConfig.preprocessedContext);
 
 
-                // Process webhook if configured
+                // Process agents if configured
                 await self.bootstrapAgents({
                   savedStreamConfig,
                   controller,
@@ -408,7 +401,6 @@ const ChatService = types
           const streamConfig = JSON.parse(savedStreamConfig);
           console.log(`chatService::handleSseStream::${streamId}::[stream configured]`);
 
-          // Create the SSE readable stream
           const stream = self.createSseReadableStream({
             streamId,
             streamConfig,
@@ -419,17 +411,14 @@ const ChatService = types
           // Use `tee()` to create two streams: one for processing and one for the response
           const [processingStream, responseStream] = stream.tee();
 
-          // Add the new stream to activeStreams
           self.setActiveStream(streamId, {
-            ...streamConfig, // Ensure streamConfig matches the expected structure
+            ...streamConfig,
           });
 
-          // Process the stream for internal logic
           processingStream.pipeTo(
               new WritableStream({
                 close() {
-                  console.log(`chatService::handleSseStream::${streamId}::[stream closed]`);
-                  self.removeActiveStream(streamId); // Use action to update state
+                  self.removeActiveStream(streamId);
                 },
               })
           );
@@ -443,7 +432,6 @@ const ChatService = types
             },
           });
         }),
-
       };
     });
 
diff --git a/workers/site/workflows/IntentService.ts b/workers/site/workflows/IntentService.ts
deleted file mode 100644
index 93438b8..0000000
--- a/workers/site/workflows/IntentService.ts
+++ /dev/null
@@ -1,64 +0,0 @@
-import type { MessageType } from "../models/Message";
-import OpenAI from "openai";
-import { z } from "zod";
-import { zodResponseFormat } from "openai/helpers/zod";
-
-const IntentSchema = z.object({
-  action: z.enum(["web-search", "news-search", "web-scrape", ""]),
-  confidence: z.number(),
-});
-
-export class SimpleSearchIntentService {
-  constructor(
-    private client: OpenAI,
-    private messages: MessageType[],
-  ) {}
-
-  async query(prompt: string, confidenceThreshold = 0.9) {
-    console.log({ confidenceThreshold });
-
-    const systemMessage = {
-      role: "system",
-      content: `Model intent as JSON:
-{
-  "action": "",
-  "confidence": ""
-}
-
-- Context from another conversation.
-- confidence is a decimal between 0 and 1 representing similarity of the context to the identified action
-- Intent reflects user's or required action.
-- Use "" for unknown/ambiguous intent.
-
-Analyze context and output JSON.`.trim(),
-    };
-
-    const conversation = this.messages.map((m) => ({
-      role: m.role,
-      content: m.content,
-    }));
-    conversation.push({ role: "user", content: prompt });
-
-    const completion = await this.client.beta.chat.completions.parse({
-      model: "gpt-4o",
-      messages: JSON.parse(JSON.stringify([systemMessage, ...conversation])),
-      temperature: 0,
-      response_format: zodResponseFormat(IntentSchema, "intent"),
-    });
-
-    const { action, confidence } = completion.choices[0].message.parsed;
-
-    console.log({ action, confidence });
-
-    return confidence >= confidenceThreshold
-      ? { action, confidence }
-      : { action: "unknown", confidence };
-  }
-}
-
-export function createIntentService(chat: {
-  messages: MessageType[];
-  openai: OpenAI;
-}) {
-  return new SimpleSearchIntentService(chat.openai, chat.messages);
-}
diff --git a/workers/site/workflows/index.ts b/workers/site/workflows/index.ts
deleted file mode 100644
index 71d4785..0000000
--- a/workers/site/workflows/index.ts
+++ /dev/null
@@ -1 +0,0 @@
-export * from "./preprocessing/executePreprocessingWorkflow";
diff --git a/workers/site/workflows/preprocessing/createPreprocessingWorkflow.ts b/workers/site/workflows/preprocessing/createPreprocessingWorkflow.ts
deleted file mode 100644
index 985bbbc..0000000
--- a/workers/site/workflows/preprocessing/createPreprocessingWorkflow.ts
+++ /dev/null
@@ -1,49 +0,0 @@
-import {
-  ManifoldRegion,
-  WorkflowFunctionManifold,
-} from "manifold-workflow-engine";
-import { createIntentService } from "../IntentService";
-import { createSearchWebhookOperator } from "./webOperator";
-import { createNewsWebhookOperator } from "./newsOperator";
-import { createScrapeWebhookOperator } from "./scrapeOperator";
-
-export const createPreprocessingWorkflow = ({
-  eventHost,
-  initialState,
-  streamId,
-  chat: { messages, openai },
-}) => {
-  const preprocessingManifold = new WorkflowFunctionManifold(
-    createIntentService({ messages, openai }),
-  );
-  preprocessingManifold.state = { ...initialState };
-
-  const searchWebhookOperator = createSearchWebhookOperator({
-    eventHost,
-    streamId,
-    openai,
-    messages,
-  });
-  const newsWebhookOperator = createNewsWebhookOperator({
-    eventHost,
-    streamId,
-    openai,
-    messages,
-  });
-  const scrapeWebhookOperator = createScrapeWebhookOperator({
-    eventHost,
-    streamId,
-    openai,
-    messages,
-  });
-
-  const preprocessingRegion = new ManifoldRegion("preprocessingRegion", [
-    searchWebhookOperator,
-    newsWebhookOperator,
-    scrapeWebhookOperator,
-  ]);
-
-  preprocessingManifold.addRegion(preprocessingRegion);
-
-  return preprocessingManifold;
-};
diff --git a/workers/site/workflows/preprocessing/executePreprocessingWorkflow.ts b/workers/site/workflows/preprocessing/executePreprocessingWorkflow.ts
deleted file mode 100644
index 83693c8..0000000
--- a/workers/site/workflows/preprocessing/executePreprocessingWorkflow.ts
+++ /dev/null
@@ -1,54 +0,0 @@
-import { createPreprocessingWorkflow } from "./createPreprocessingWorkflow";
-
-export async function executePreprocessingWorkflow({
-  latestUserMessage,
-  latestAiMessage,
-  eventHost,
-  streamId,
-  chat: { messages, openai },
-}) {
-  console.log(`Executing executePreprocessingWorkflow`);
-  const initialState = { latestUserMessage, latestAiMessage };
-
-  // Add execution tracking flag to prevent duplicate runs
-  const executionKey = `preprocessing-${crypto.randomUUID()}`;
-  if (globalThis[executionKey]) {
-    console.log("Preventing duplicate preprocessing workflow execution");
-    return globalThis[executionKey];
-  }
-
-  const workflows = {
-    preprocessing: createPreprocessingWorkflow({
-      eventHost,
-      initialState,
-      streamId,
-      chat: { messages, openai },
-    }),
-    results: new Map(),
-  };
-
-  try {
-    // Store the promise to prevent parallel executions
-    globalThis[executionKey] = (async () => {
-      await workflows.preprocessing.navigate(latestUserMessage);
-      await workflows.preprocessing.executeWorkflow(latestUserMessage);
-      console.log(
-        `executePreprocessingWorkflow::workflow::preprocessing::results`,
-        { state: JSON.stringify(workflows.preprocessing.state, null, 2) },
-      );
-      workflows.results.set("preprocessed", workflows.preprocessing.state);
-
-      // Cleanup after execution
-      setTimeout(() => {
-        delete globalThis[executionKey];
-      }, 1000);
-
-      return workflows;
-    })();
-
-    return await globalThis[executionKey];
-  } catch (error) {
-    delete globalThis[executionKey];
-    throw new Error("Workflow execution failed");
-  }
-}
diff --git a/workers/site/workflows/preprocessing/newsOperator.ts b/workers/site/workflows/preprocessing/newsOperator.ts
deleted file mode 100644
index 80bbecd..0000000
--- a/workers/site/workflows/preprocessing/newsOperator.ts
+++ /dev/null
@@ -1,101 +0,0 @@
-import { WorkflowOperator } from "manifold-workflow-engine";
-import { zodResponseFormat } from "openai/helpers/zod";
-import { z } from "zod";
-
-const QuerySchema = z.object({
-  query: z.string(),
-});
-
-export function createNewsWebhookOperator({
-  eventHost,
-  streamId,
-  openai,
-  messages,
-}) {
-  return new WorkflowOperator("news-search", async (state: any) => {
-    const { latestUserMessage } = state;
-    console.log(`Processing user message: ${latestUserMessage}`);
-
-    const resource = "news-search";
-    const input = await getQueryFromContext({
-      openai,
-      messages,
-      latestUserMessage,
-    });
-
-    const eventSource = new URL(eventHost);
-    const url = `${eventSource}api/webhooks`;
-    console.log({ url });
-
-    const stream = {
-      id: crypto.randomUUID(),
-      parent: streamId,
-      resource,
-      payload: input,
-    };
-    const createStreamResponse = await fetch(`${eventSource}api/webhooks`, {
-      method: "POST",
-      headers: {
-        "Content-Type": "application/json",
-      },
-      body: JSON.stringify({
-        id: stream.id,
-        parent: streamId,
-        resource: "news-search",
-        payload: {
-          input,
-        },
-      }),
-    });
-    const raw = await createStreamResponse.text();
-    const { stream_url } = JSON.parse(raw);
-    const surl = eventHost + stream_url;
-    const webhook = { url: surl, id: stream.id, resource };
-
-    return {
-      ...state,
-      webhook,
-      latestUserMessage: "",
-      latestAiMessage: "",
-    };
-  });
-
-  async function getQueryFromContext({ messages, openai, latestUserMessage }) {
-    const systemMessage = {
-      role: "system",
-      content: `Analyze the latest message in a conversation and generate a JSON object with a single implied question for a news search. The JSON should be structured as follows:
-
-{
-  "query": "<question to be answered by a news search>"
-}
-
-## Example
-{
-  "query": "When was the last Buffalo Sabres hockey game?"
-}
-
-Focus on the most recent message to determine the query. Output only the JSON object without any additional text.`,
-    };
-
-    const conversation = messages.map((m) => ({
-      role: m.role,
-      content: m.content,
-    }));
-    conversation.push({ role: "user", content: `${latestUserMessage}` });
-
-    const m = [systemMessage, ...conversation];
-
-    const completion = await openai.beta.chat.completions.parse({
-      model: "gpt-4o-mini",
-      messages: m,
-      temperature: 0,
-      response_format: zodResponseFormat(QuerySchema, "query"),
-    });
-
-    const { query } = completion.choices[0].message.parsed;
-
-    console.log({ newsWebhookQuery: query });
-
-    return query;
-  }
-}
diff --git a/workers/site/workflows/preprocessing/scrapeOperator.ts b/workers/site/workflows/preprocessing/scrapeOperator.ts
deleted file mode 100644
index 19a5314..0000000
--- a/workers/site/workflows/preprocessing/scrapeOperator.ts
+++ /dev/null
@@ -1,112 +0,0 @@
-import { WorkflowOperator } from "manifold-workflow-engine";
-import { zodResponseFormat } from "openai/helpers/zod";
-import { z } from "zod";
-
-const UrlActionSchema = z.object({
-  url: z.string(),
-  query: z.string(),
-  action: z.enum(["read", "scrape", "crawl", ""]),
-});
-
-export function createScrapeWebhookOperator({
-  eventHost,
-  streamId,
-  openai,
-  messages,
-}) {
-  return new WorkflowOperator("web-scrape", async (state: any) => {
-    const { latestUserMessage } = state;
-
-    const webscrapeWebhookEndpoint = "/api/webhooks";
-
-    const resource = "web-scrape";
-    const context = await getQueryFromContext({
-      openai,
-      messages,
-      latestUserMessage,
-    });
-
-    const input = {
-      url: context?.url,
-      action: context?.action,
-      query: context.query,
-    };
-
-    const eventSource = new URL(eventHost);
-    const url = `${eventSource}api/webhooks`;
-
-    const stream = {
-      id: crypto.randomUUID(),
-      parent: streamId,
-      resource,
-      payload: input,
-    };
-    const createStreamResponse = await fetch(`${eventSource}api/webhooks`, {
-      method: "POST",
-      headers: {
-        "Content-Type": "application/json",
-      },
-      body: JSON.stringify({
-        id: stream.id,
-        parent: streamId,
-        resource: "web-scrape",
-        payload: {
-          input,
-        },
-      }),
-    });
-    const raw = await createStreamResponse.text();
-    const { stream_url } = JSON.parse(raw);
-    const surl = eventHost + stream_url;
-    const webhook = { url: surl, id: stream.id, resource };
-
-    return {
-      ...state,
-      webhook,
-      latestUserMessage: "",
-      latestAiMessage: "",
-    };
-  });
-}
-
-async function getQueryFromContext({ messages, openai, latestUserMessage }) {
-  const systemMessage = {
-    role: "system" as const,
-    content:
-      `You are modeling a structured output containing a single question, a URL, and an action, all relative to a single input.
-
-Return the result as a JSON object in the following structure:
-{
-  "url": "Full URL in the conversation that references the URL being interacted with. No trailing slash!",
-  "query": "Implied question about the resources at the URL.",
-  "action": "read | scrape | crawl"
-}
-
-- The input being modeled is conversational data from a different conversation than this one.
-- Intent should represent a next likely action the system might take to satisfy or enhance the user's request.
-
-Instructions:
-1. Analyze the provided context and declare the url, action, and question implied by the latest message.
-
-Output the JSON object. Do not include any additional explanations or text.`.trim(),
-  };
-
-  const conversation = messages.map((m) => ({
-    role: m.role,
-    content: m.content,
-  }));
-  conversation.push({ role: "user", content: `${latestUserMessage}` });
-
-  const m = [systemMessage, ...conversation];
-
-  const completion = await openai.beta.chat.completions.parse({
-    model: "gpt-4o-mini",
-    messages: m,
-    temperature: 0,
-    response_format: zodResponseFormat(UrlActionSchema, "UrlActionSchema"),
-  });
-
-  const { query, action, url } = completion.choices[0].message.parsed;
-
-  return { query, action, url };
-}
diff --git a/workers/site/workflows/preprocessing/webOperator.ts b/workers/site/workflows/preprocessing/webOperator.ts
deleted file mode 100644
index a2b4b6c..0000000
--- a/workers/site/workflows/preprocessing/webOperator.ts
+++ /dev/null
@@ -1,100 +0,0 @@
-import { WorkflowOperator } from "manifold-workflow-engine";
-import { zodResponseFormat } from "openai/helpers/zod";
-import { z } from "zod";
-
-const QuerySchema = z.object({
-  query: z.string(), // No min/max constraints in the schema
-});
-
-export function createSearchWebhookOperator({
-  eventHost,
-  streamId,
-  openai,
-  messages,
-}) {
-  return new WorkflowOperator("web-search", async (state: any) => {
-    const { latestUserMessage } = state;
-
-    const websearchWebhookEndpoint = "/api/webhooks";
-
-    const resource = "web-search";
-    const input = await getQueryFromContext({
-      openai,
-      messages,
-      latestUserMessage,
-    });
-
-    // process webhooks
-    const eventSource = new URL(eventHost);
-    const url = `${eventSource}api/webhooks`;
-
-    const stream = {
-      id: crypto.randomUUID(),
-      parent: streamId,
-      resource,
-      payload: input,
-    };
-    const createStreamResponse = await fetch(`${eventSource}api/webhooks`, {
-      method: "POST",
-      headers: {
-        "Content-Type": "application/json",
-      },
-      body: JSON.stringify({
-        id: stream.id,
-        parent: streamId,
-        resource: "web-search",
-        payload: {
-          input,
-        },
-      }),
-    });
-    const raw = await createStreamResponse.text();
-    const { stream_url } = JSON.parse(raw);
-    const surl = eventHost + stream_url;
-    const webhook = { url: surl, id: stream.id, resource };
-
-    return {
-      ...state,
-      webhook,
-      latestUserMessage: "", // unset to break out of loop
-      latestAiMessage: "", // unset to break out of loop
-    };
-  });
-}
-
-async function getQueryFromContext({ messages, openai, latestUserMessage }) {
-  const systemMessage = {
-    role: "system",
-    content: `Analyze the latest message in the conversation and generate a JSON object with a single implied question for a web search. The JSON should be structured as follows:
-
-{
-  "query": "the question that needs a web search"
-}
-
-## Example
-{
-  "query": "What was the score of the last Buffalo Sabres hockey game?"
-}
-
-Focus on the most recent message to determine the query. Output only the JSON object without any additional text.`,
-  };
-
-  const conversation = messages.map((m) => ({
-    role: m.role,
-    content: m.content,
-  }));
-  conversation.push({ role: "user", content: `${latestUserMessage}` });
-
-  const m = [systemMessage, ...conversation];
-
-  const completion = await openai.beta.chat.completions.parse({
-    model: "gpt-4o-mini",
-    messages: m,
-    temperature: 0,
-    response_format: zodResponseFormat(QuerySchema, "query"),
-  });
-
-  const { query } = completion.choices[0].message.parsed;
-
-  return query;
-}