typo: fix missing backtick

public/_redirects

@@ -96,6 +96,9 @@
 /access/ssh/short-live-cert-server/ /cloudflare-one/connections/connect-networks/use-cases/ssh/ssh-infrastructure-access/ 301
 /access/ssh/ssh-guide/ /cloudflare-one/connections/connect-networks/use-cases/ssh/ 301
 
+# agents
+/agents/build/prompts/ /agents/building-with-ai/prompting/ 301
+
 # ai
 /ai/ /use-cases/ai/ 301
 

src/content/docs/agents/api-reference/configuration.mdx

@@ -0,0 +1,72 @@
+---
+title: Configuration
+pcx_content_type: concept
+sidebar:
+  order: 3
+---
+
+import { MetaInfo, Render, Type, WranglerConfig } from "~/components";
+
+An Agent is configured like any other Cloudflare Workers project, and uses [a wrangler configuration](/workers/wrangler/configuration/) file to define where your code is and what services (bindings) it will use.
+
+The typical file structure for an Agent project created from `npm create cloudflare@latest -- --template cloudflare/agents` follows:
+
+```sh
+.
+|-- package-lock.json
+|-- package.json
+|-- public
+|   `-- index.html
+|-- src
+|   `-- index.ts // your Agent definition
+|-- test
+|   |-- index.spec.ts // your tests
+|   `-- tsconfig.json
+|-- tsconfig.json
+|-- vitest.config.mts
+|-- worker-configuration.d.ts
+`-- wrangler.jsonc // your Workers & Agent configuration
+```
+
+Below is a minimal `wrangler.jsonc` file that defines the configuration for an Agent, including the entry point,  `durable_object` namespace, and code `migrations`:
+
+<WranglerConfig>
+
+```jsonc
+{
+	"$schema": "node_modules/wrangler/config-schema.json",
+	"name": "agents-example",
+	"main": "src/index.ts",
+	"compatibility_date": "2025-02-23",
+	"compatibility_flags": ["nodejs_compat"],
+	"durable_objects": {
+		"bindings": [
+			{
+				// Required:
+				"name": "MyAgent", // How your Agent is called from your Worker
+				"class_name": "MyAgent", // Must match the class name of the Agent in your code
+				// Optional: set this if the Agent is defined in another Worker script
+				"script_name": "the-other-worker"
+			},
+		],
+	},
+	"migrations": [
+		{
+			"tag": "v1",
+			// Mandatory for the Agent to store state
+			"new_sqlite_classes": ["MyAgent"],
+		},
+	],
+	"observability": {
+		"enabled": true,
+	},
+}
+```
+
+</WranglerConfig>
+
+The configuration includes:
+
+- A `main` field that points to the entry point of your Agent, which is typically a TypeScript (or JavaScript) file.
+- A `durable_objects` field that defines the [Durable Object namespace](/durable-objects/reference/glossary/) that your Agents will run within.
+- A `migrations` field that defines the code migrations that your Agent will use. This field is mandatory and must contain at least one migration. The `new_sqlite_classes` field is mandatory for the Agent to store state.

src/content/docs/agents/api-reference/index.mdx

@@ -0,0 +1,13 @@
+---
+title: Agents API Reference
+pcx_content_type: navigation
+sidebar:
+  order: 3
+  group:
+    hideIndex: true
+
+---
+
+import { DirectoryListing } from "~/components"
+
+<DirectoryListing />

src/content/docs/agents/api-reference/sdk.mdx

@@ -0,0 +1,34 @@
+---
+title: Agents SDK
+pcx_content_type: concept
+sidebar:
+  order: 2
+
+---
+
+import { MetaInfo, Render, Type, WranglerConfig } from "~/components";
+
+At its most basic, an Agent is a JavaScript class that extends the `Agent` class from the `@cloudflare/agents` package. An Agent encapsulates all of the logic for an Agent, including how clients can connect to it, how it stores state, the methods it exposes, and any error handling.
+
+<TypeScriptExample>
+
+```ts
+import { Agent } from "@cloudflare/agents";
+
+class MyAgent extends Agent {
+	// Define methods on the Agent
+}
+
+export default MyAgent;
+```
+
+</TypeScriptExample>
+
+An Agent can have many (millions of) instances: each instance is a separate micro-server that runs independently of the others. This allows Agents to scale horizontally: an Agent can be associated with a single user, or many thousands of users, depending on the agent you're building.
+
+Instances of an Agent are addressed by a unique identifier: that identifier (ID) can be the user ID, an email address, GitHub username, a flight ticket number, an invoice ID, or any other identifier that helps to uniquely identify the instance and for whom it is acting on behalf of.
+
+## Agent
+
+TODO - agent class
+

src/content/docs/agents/building-with-ai/index.mdx

@@ -0,0 +1,13 @@
+---
+title: Building with AI
+pcx_content_type: navigation
+sidebar:
+  order: 5
+  group:
+    hideIndex: true
+
+---
+
+import { DirectoryListing } from "~/components"
+
+<DirectoryListing />

src/content/docs/agents/build/llms.txt.mdx → src/content/docs/agents/building-with-ai/llms.txt.mdx

@@ -3,7 +3,7 @@ pcx_content_type: navigation
 title: llms.txt
 external_link: /llms.txt
 sidebar:
-  order: 20
+  order: 102
 head: []
 description: Provide context to your AI models & tools when building on Cloudflare.
 ---

src/content/docs/agents/build/prompts.mdx → src/content/docs/agents/building-with-ai/prompting.mdx

@@ -2,7 +2,7 @@
 title: Prompting AI Models
 pcx_content_type: concept
 sidebar:
-  order: 2
+  order: 101
 ---
 
 import { Tabs, TabItem, GlossaryTooltip, Type, Badge, TypeScriptExample } from "~/components";

src/content/docs/agents/concepts/calling-llms.mdx

@@ -0,0 +1,31 @@
+---
+title: Calling LLMs
+pcx_content_type: concept
+sidebar:
+  order: 6
+
+---
+
+import { Render } from "~/components";
+
+### Understanding LLM Providers and Model Types
+
+Different LLM providers offer models optimized for specific types of tasks. When building AI systems, choosing the right model is crucial for both performance and cost efficiency.
+
+#### Reasoning Models
+Models like OpenAI's o1, Anthropic's Claude, and DeepSeek's R1 are particularly well-suited for complex reasoning tasks. These models excel at:
+- Breaking down problems into steps
+- Following complex instructions
+- Maintaining context across long conversations
+- Generating code and technical content
+
+For example, when implementing a travel booking system, you might use a reasoning model to analyze travel requirements and generate appropriate booking strategies.
+
+#### Instruction Models
+Models like GPT-4 and Claude Instant are optimized for following straightforward instructions efficiently. They work well for:
+- Content generation
+- Simple classification tasks
+- Basic question answering
+- Text transformation
+
+These models are often more cost-effective for straightforward tasks that don't require complex reasoning.