haijohn · December 7, 2025 13:28
diff --git a/gistfile1.txt b/gistfile1.txt
 Architecture Design: Autonomous API Agent Platform
 1. System Overview
 The system is a "ReAct" (Reasoning + Acting) Agent platform. It ingests OpenAPI specifications (Swagger) from an Admin, converts them into "Tools," and allows an LLM to orchestrate HTTP requests to fulfill natural language user intents.

 high-Level Context Diagram (Mermaid)
 Code snippet

 graph TD
    User((User)) -->|Natural Language| FE[Frontend UI]
    Admin((Admin)) -->|OpenAPI Specs / Prompts| FE
    FE -->|REST/WebSocket| Gateway[API Gateway / Orchestrator]
    
    subgraph "Agent Backend Core"
        Gateway --> Manager[Session Manager]
        Manager -->|Get Context| DB[(Database / Vector Store)]
        Manager -->|Reasoning| LLM[LLM Service \n(e.g., GPT-4o, Claude 3.5)]
        Manager -->|Execute Tool| Executor[API Executor Engine]
        Executor -->|Validate & Call| ExtAPI[External REST APIs]
    end
    
    ExtAPI -->|JSON Response/Error| Executor
    Executor -->|Observation| Manager
    Manager -->|Recursion| LLM
 2. Core Components
 A. Data Model (The "Knowledge")
 To make the agent work, we need to map API definitions to a structure the LLM understands.

 1. Agent Configuration

 System Prompt: The persona (e.g., "You are a DevOps assistant...").

 Tool Registry: A collection of available API endpoints.

 2. Tool Definition (The Schema) Instead of raw code, we store tools as structured JSON. This is often derived from parsing an uploaded OpenAPI (Swagger) file.

 JSON

 {
  "tool_name": "create_user",
  "description": "Creates a new user in the system. Use this when the user asks to sign someone up.",
  "method": "POST",
  "url_template": "https://api.example.com/users",
  "parameters_schema": {
    "type": "object",
    "properties": {
      "username": {"type": "string", "description": "The desired login name"},
      "role": {"type": "string", "enum": ["admin", "user"]}
    },
    "required": ["username"]
  },
  "auth_config": { "type": "bearer", "token_env_key": "API_KEY_1" }
 }
 B. The Orchestrator (The "Brain")
 This is the application logic that manages the conversation loop. It typically runs a State Machine.

 State 1: Context Assembly: Fetch conversation history + relevant Tools.

 State 2: Reasoning (LLM Call): Send User Input + Tools to LLM.

 State 3: Routing:

 If LLM returns text -> Return to User.

 If LLM returns tool_call -> Go to Executor.

 State 4: Execution: Run the API call.

 State 5: Observation: Append the API result (success OR error) to the chat history. Return to State 2.

 C. The Executor Engine
 This component is a "dumb" HTTP client with safety rails.

 Input: URL, Method, Headers, JSON Body (provided by LLM).

 Logic:

 Inject Authentication headers (from secure storage).

 Execute HTTP Request.

 Sanitization: Truncate massive JSON responses (to save tokens) before sending back to LLM.

 Error Handling: Catch 4xx/5xx errors and return them as text to the Orchestrator, not as exceptions. This allows the LLM to read the error and try again.

 3. Detailed Execution Flow (Sequence Diagram)
 This diagram illustrates "Chaining" and "Error Recovery."

 Scenario: User says "Find the user 'Bob' and delete him."

 Requirement: Agent must GET /users?name=Bob, extract the ID, then DELETE /users/{ID}.

 Code snippet

 sequenceDiagram
    participant U as User
    participant O as Orchestrator
    participant L as LLM (Brain)
    participant E as API Executor
    participant X as External API

    U->>O: "Find Bob and delete him"
    
    loop Reasoning Loop
        O->>L: Prompt: History + Tools + "Find Bob and delete him"
        L->>O: Response: Call Tool `search_users({name: 'Bob'})`
        
        O->>E: Execute `GET /users?name=Bob`
        E->>X: HTTP GET Request
        X->>E: 200 OK `[{id: 101, name: "Bob"}]`
        E->>O: Observation: `Found: [{id: 101, name: "Bob"}]`
        
        Note right of O: Loop continues (Chaining)
        
        O->>L: Prompt: History + Observation + "What next?"
        L->>O: Response: Call Tool `delete_user({id: 101})`
        
        O->>E: Execute `DELETE /users/101`
        E->>X: HTTP DELETE
        X->>E: 403 Forbidden (Simulated Error)
        E->>O: Observation: `Error: 403 Forbidden`
        
        Note right of O: Loop continues (Error Handling)
        
        O->>L: Prompt: History + "Error: 403" + "What next?"
        L->>O: Response: Text "I found Bob (ID 101), but I don't have permission to delete him."
    end
    
    O->>U: Final Response
 4. Implementation Guide for Engineers
 Phase 1: The "Tool" Parser
 You need a service that ingests standard definitions.

 Input: URL to swagger.json or openapi.yaml.

 Action: Parse the file. For every path/method combination, generate a JSON Schema description.

 Storage: Save these schemas in your database linked to the specific Agent.

 Phase 2: The Loop Logic (Pseudo-Code)
 This is the core logic your backend engineer needs to write.

 Python

 MAX_ITERATIONS = 5

 def run_agent_loop(user_input, chat_history, available_tools):
    messages = chat_history + [{"role": "user", "content": user_input}]
    
    for _ in range(MAX_ITERATIONS):
        # 1. Ask LLM what to do
        response = llm.chat_completion(
            messages=messages,
            tools=available_tools # Function calling definitions
        )
        
        message = response.choices[0].message
        messages.append(message) # Update history state

        # 2. Check if LLM wants to run a tool
        if message.tool_calls:
            for tool_call in message.tool_calls:
                # 3. Decode arguments (e.g. JSON string to dict)
                func_name = tool_call.function.name
                args = json.loads(tool_call.function.arguments)
                
                # 4. EXECUTE API (The "Acting" part)
                try:
                    api_result = http_client.request(func_name, args)
                except Exception as e:
                    api_result = f"Error executing request: {str(e)}"

                # 5. Feed result back to LLM (The "Observation")
                messages.append({
                    "role": "tool",
                    "tool_call_id": tool_call.id,
                    "content": json.dumps(api_result)
                })
        else:
            # LLM provided a final text answer
            return message.content
            
    return "Error: Agent got stuck in a loop."
 Phase 3: Error Handling Strategy
 The agent must treat errors as data, not exceptions.

 Network Level: If the external API is down (Connection Refused), return a system message: "System Error: API Unreachable". The LLM might say "I can't connect right now."

 Application Level: If the API returns 400 Bad Request, return the body: {"error": "Invalid ID format"}. The LLM will read this and can self-correct: "Ah, I used the wrong ID format, let me try again..."

 5. Security & Safety (Critical)
 Human-in-the-Loop (HITL): For dangerous actions (POST/DELETE), the architecture should support a "Approval" state.

 LLM: "I want to delete user 101."

 System: Pauses. Sends UI prompt to User: "Agent wants to DELETE user 101. Allow?"

 User: Clicks "Yes".

 System: Resumes loop.

 Output Sanitization: APIs can return massive JSON blobs (1MB+). This will crash your LLM context window. The Executor must summarize or truncate data (e.g., "Response too long, first 5 items: [...]").

 Authentication Storage: Never store API keys in the prompt. Store them in a secure Vault (AWS Secrets Manager / HashiCorp Vault) and inject them inside the Executor code only at the moment of the request.

 6. Recommended Tech Stack
 LLM Model: OpenAI GPT-4o or Anthropic Claude 3.5 Sonnet (Claude is excellent at tool use and coding).

 Backend: Python (FastAPI/LangGraph) or TypeScript (Node.js/LangChain.js).

 Orchestration Framework:

 LangGraph (Python): Highly recommended for this specific state-machine architecture. It handles the looping and state management natively.

 Temporal.io: If you need high reliability for long-running agent tasks.
	Architecture Design: Autonomous API Agent Platform
	1. System Overview
	The system is a "ReAct" (Reasoning + Acting) Agent platform. It ingests OpenAPI specifications (Swagger) from an Admin, converts them into "Tools," and allows an LLM to orchestrate HTTP requests to fulfill natural language user intents.

	high-Level Context Diagram (Mermaid)
	Code snippet

	graph TD
	User((User)) -->\|Natural Language\| FE[Frontend UI]
	Admin((Admin)) -->\|OpenAPI Specs / Prompts\| FE
	FE -->\|REST/WebSocket\| Gateway[API Gateway / Orchestrator]

	subgraph "Agent Backend Core"
	Gateway --> Manager[Session Manager]
	Manager -->\|Get Context\| DB[(Database / Vector Store)]
	Manager -->\|Reasoning\| LLM[LLM Service \n(e.g., GPT-4o, Claude 3.5)]
	Manager -->\|Execute Tool\| Executor[API Executor Engine]
	Executor -->\|Validate & Call\| ExtAPI[External REST APIs]
	end

	ExtAPI -->\|JSON Response/Error\| Executor
	Executor -->\|Observation\| Manager
	Manager -->\|Recursion\| LLM
	2. Core Components
	A. Data Model (The "Knowledge")
	To make the agent work, we need to map API definitions to a structure the LLM understands.

	1. Agent Configuration

	System Prompt: The persona (e.g., "You are a DevOps assistant...").

	Tool Registry: A collection of available API endpoints.

	2. Tool Definition (The Schema) Instead of raw code, we store tools as structured JSON. This is often derived from parsing an uploaded OpenAPI (Swagger) file.

	JSON

	{
	"tool_name": "create_user",
	"description": "Creates a new user in the system. Use this when the user asks to sign someone up.",
	"method": "POST",
	"url_template": "https://api.example.com/users",
	"parameters_schema": {
	"type": "object",
	"properties": {
	"username": {"type": "string", "description": "The desired login name"},
	"role": {"type": "string", "enum": ["admin", "user"]}
	},
	"required": ["username"]
	},
	"auth_config": { "type": "bearer", "token_env_key": "API_KEY_1" }
	}
	B. The Orchestrator (The "Brain")
	This is the application logic that manages the conversation loop. It typically runs a State Machine.

	State 1: Context Assembly: Fetch conversation history + relevant Tools.

	State 2: Reasoning (LLM Call): Send User Input + Tools to LLM.

	State 3: Routing:

	If LLM returns text -> Return to User.

	If LLM returns tool_call -> Go to Executor.

	State 4: Execution: Run the API call.

	State 5: Observation: Append the API result (success OR error) to the chat history. Return to State 2.

	C. The Executor Engine
	This component is a "dumb" HTTP client with safety rails.

	Input: URL, Method, Headers, JSON Body (provided by LLM).

	Logic:

	Inject Authentication headers (from secure storage).

	Execute HTTP Request.

	Sanitization: Truncate massive JSON responses (to save tokens) before sending back to LLM.

	Error Handling: Catch 4xx/5xx errors and return them as text to the Orchestrator, not as exceptions. This allows the LLM to read the error and try again.

	3. Detailed Execution Flow (Sequence Diagram)
	This diagram illustrates "Chaining" and "Error Recovery."

	Scenario: User says "Find the user 'Bob' and delete him."

	Requirement: Agent must GET /users?name=Bob, extract the ID, then DELETE /users/{ID}.

	Code snippet

	sequenceDiagram
	participant U as User
	participant O as Orchestrator
	participant L as LLM (Brain)
	participant E as API Executor
	participant X as External API

	U->>O: "Find Bob and delete him"

	loop Reasoning Loop
	O->>L: Prompt: History + Tools + "Find Bob and delete him"
	L->>O: Response: Call Tool `search_users({name: 'Bob'})`

	O->>E: Execute `GET /users?name=Bob`
	E->>X: HTTP GET Request
	X->>E: 200 OK `[{id: 101, name: "Bob"}]`
	E->>O: Observation: `Found: [{id: 101, name: "Bob"}]`

	Note right of O: Loop continues (Chaining)

	O->>L: Prompt: History + Observation + "What next?"
	L->>O: Response: Call Tool `delete_user({id: 101})`

	O->>E: Execute `DELETE /users/101`
	E->>X: HTTP DELETE
	X->>E: 403 Forbidden (Simulated Error)
	E->>O: Observation: `Error: 403 Forbidden`

	Note right of O: Loop continues (Error Handling)

	O->>L: Prompt: History + "Error: 403" + "What next?"
	L->>O: Response: Text "I found Bob (ID 101), but I don't have permission to delete him."
	end

	O->>U: Final Response
	4. Implementation Guide for Engineers
	Phase 1: The "Tool" Parser
	You need a service that ingests standard definitions.

	Input: URL to swagger.json or openapi.yaml.

	Action: Parse the file. For every path/method combination, generate a JSON Schema description.

	Storage: Save these schemas in your database linked to the specific Agent.

	Phase 2: The Loop Logic (Pseudo-Code)
	This is the core logic your backend engineer needs to write.

	Python

	MAX_ITERATIONS = 5

	def run_agent_loop(user_input, chat_history, available_tools):
	messages = chat_history + [{"role": "user", "content": user_input}]

	for _ in range(MAX_ITERATIONS):
	# 1. Ask LLM what to do
	response = llm.chat_completion(
	messages=messages,
	tools=available_tools # Function calling definitions
	)

	message = response.choices[0].message
	messages.append(message) # Update history state

	# 2. Check if LLM wants to run a tool
	if message.tool_calls:
	for tool_call in message.tool_calls:
	# 3. Decode arguments (e.g. JSON string to dict)
	func_name = tool_call.function.name
	args = json.loads(tool_call.function.arguments)

	# 4. EXECUTE API (The "Acting" part)
	try:
	api_result = http_client.request(func_name, args)
	except Exception as e:
	api_result = f"Error executing request: {str(e)}"

	# 5. Feed result back to LLM (The "Observation")
	messages.append({
	"role": "tool",
	"tool_call_id": tool_call.id,
	"content": json.dumps(api_result)
	})
	else:
	# LLM provided a final text answer
	return message.content

	return "Error: Agent got stuck in a loop."
	Phase 3: Error Handling Strategy
	The agent must treat errors as data, not exceptions.

	Network Level: If the external API is down (Connection Refused), return a system message: "System Error: API Unreachable". The LLM might say "I can't connect right now."

	Application Level: If the API returns 400 Bad Request, return the body: {"error": "Invalid ID format"}. The LLM will read this and can self-correct: "Ah, I used the wrong ID format, let me try again..."

	5. Security & Safety (Critical)
	Human-in-the-Loop (HITL): For dangerous actions (POST/DELETE), the architecture should support a "Approval" state.

	LLM: "I want to delete user 101."

	System: Pauses. Sends UI prompt to User: "Agent wants to DELETE user 101. Allow?"

	User: Clicks "Yes".

	System: Resumes loop.

	Output Sanitization: APIs can return massive JSON blobs (1MB+). This will crash your LLM context window. The Executor must summarize or truncate data (e.g., "Response too long, first 5 items: [...]").

	Authentication Storage: Never store API keys in the prompt. Store them in a secure Vault (AWS Secrets Manager / HashiCorp Vault) and inject them inside the Executor code only at the moment of the request.

	6. Recommended Tech Stack
	LLM Model: OpenAI GPT-4o or Anthropic Claude 3.5 Sonnet (Claude is excellent at tool use and coding).

	Backend: Python (FastAPI/LangGraph) or TypeScript (Node.js/LangChain.js).

	Orchestration Framework:

	LangGraph (Python): Highly recommended for this specific state-machine architecture. It handles the looping and state management natively.

	Temporal.io: If you need high reliability for long-running agent tasks.
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