Autonomous Agent Compute & Execution

Deploy multi-turn reasoning loops, autonomous coding workflows, web intelligence agents, and tool-augmented workflows powered by Claude Opus 4.6, DeepSeek R1, and Google Gemini.

The Agent Compute Engine (server/agent-compute/ on port 8795) orchestrates dynamic tool calling, skill execution, persistent workspace file storage, real-time Server-Sent Events (SSE) streaming, and Human-in-the-Loop controls.

API Environment

Base URL: https://apis.fotohub.app/compute/v1
Region: eu-central-1 (Frankfurt, AWS)
Billing: 100% USD prepaid wallet. NO PLN, NO credits. Minimum $0.50 balance to provision agents.
Auth: Authorization: Bearer fh_live_YOUR_API_KEY

Agent Lifecycle State Machine

The FOTOhub autonomous agent execution follows a rigorous state machine, ensuring deterministic progress, strict budget constraints, and secure boundaries for sandbox executions.

mermaid

stateDiagram-v2
    [*] --> created: POST /compute/v1/tasks/create
    created --> planning: Initialize Engine & Context
    planning --> executing_step: Select Tool / Action
    
    executing_step --> tool_running: Dispatch to Sandbox / Web
    tool_running --> evaluating_result: Tool Completed
    evaluating_result --> executing_step: Next Step Needed
    
    executing_step --> awaiting_approval: Requires Human Clarification
    awaiting_approval --> executing_step: POST /compute/v1/tasks/{id}/respond
    
    evaluating_result --> completed: Goal Satisfied
    executing_step --> failed: Unrecoverable Error
    awaiting_approval --> cancelled: User Cancels
    
    completed --> [*]
    failed --> [*]
    cancelled --> [*]

Architecture Deep Dive: Agent-Compute Orchestrator

The FOTOhub Agent-Compute Orchestrator is designed for high-throughput, low-latency agentic loops. It runs on our proprietary event-driven infrastructure, ensuring that every tool invocation, context window update, and streaming output is processed efficiently.

The Tool Calling Loop

At the heart of the orchestrator is the Tool Calling Loop. When an agent receives an objective, the orchestrator:

Context Initialization: Loads the prompt, system instructions, and available tools into the model's context window.
Action Generation: The model predicts the next best action (e.g., call a tool or yield the final answer).
Sandbox Dispatch: If a tool requires execution (like Python code), the orchestrator securely dispatches it to the Firecracker microVM endpoint (https://apis.fotohub.app/sandbox/exec-python).
Result Ingestion: The output from the tool (stdout, stderr, or structured JSON) is appended to the context window.
Re-evaluation: The loop restarts until the model dictates the goal is met or the max_steps limit is hit.

Firecracker MicroVM Execution

All arbitrary code execution generated by agents happens within ephemeral Firecracker microVMs. These sandboxes are completely isolated from the FOTOhub control plane.

Fast Boot Times: MicroVMs boot in under 150ms.
Resource Constraints: Each VM is hard-capped on RAM and CPU based on your selected tier.
Network Isolation: By default, sandboxes have restricted egress unless explicitly granted web access.

Available Agent Models & Pricing

FOTOhub provides access to industry-leading models, each optimized for different agentic tasks.

USD Billing Only

All services on FOTOhub are billed strictly in USD against your prepaid wallet. There are no credits or alternative currencies accepted (NO PLN). You must maintain a minimum of $0.50 to provision compute resources.

Language Models

Model	Provider	Context Window	Best For	Price (Input / 1M)	Price (Output / 1M)
Claude 3 Opus (4.6)	Anthropic	200K tokens	Complex reasoning, large refactors, autonomous software engineering	$15.00	$75.00
Claude 3.5 Sonnet	Anthropic	200K tokens	Fast coding, tool use, web automation	$3.00	$15.00
DeepSeek R1	DeepSeek	128K tokens	Advanced mathematical reasoning, logic puzzles, optimization algorithms	$1.00	$3.00
Gemini 2.0 Flash	Google	1M tokens	High-volume document analysis, multimodal processing	$0.35	$1.05
GPT-4o	OpenAI	128K tokens	General purpose, versatile text/vision tasks, reliable tool calling	$5.00	$15.00

Dedicated Compute Instances

Agents can provision dedicated GPU/CPU resources on-demand or spot for background workloads. Prices are listed per hour.

Instance Family	Specs	Spot Price ($/hr)	On-Demand Price ($/hr)
G5.xlarge	A10G (24GB VRAM)	$0.38	$1.01
G4dn.xlarge	T4 (16GB VRAM)	$0.20	$0.53
T3, C5, M5, R5	CPU optimized	Varies	Varies

Storage & Egress

FOTOhub S3 (s1.fotohub.app): $0.0245 / GB-month. Intra-cluster egress is FREE.
EBS gp3: $0.08 / GB-month.
EBS io2: $0.125 / GB-month (Supports up to 64K IOPS).

Tool Categories Available to Agents

Agents are heavily augmented with a suite of native tools. You can specify which tools are available during task creation using the skills array.

1. File Operations

Agents can read, write, patch, and delete files within their persistent workspace.

read_file: Load contents up to 10MB.
write_to_file: Overwrite or create new files.
replace_file_content: Precise regex or line-range replacements.
list_dir: Traverse directory structures.

2. Code Execution (Sandbox)

Agents can execute code in a secure Firecracker sandbox via https://apis.fotohub.app/sandbox/exec-python.

execute_python: Run Python scripts with pre-installed data science packages.
execute_bash: Run shell commands, install apt packages ephemerally.
execute_nodejs: Run JavaScript/TypeScript tasks.

3. Web Search & Intelligence

search_web: Perform Google-like searches to find real-time information.
read_url: Scrape and extract markdown content from public URLs.
read_browser_page: Headless browser access for scraping SPA/React applications.

4. FOTOhub Native API Integrations

provision_instance: Spin up a G4dn or G5 instance directly from an agent.
upload_to_s3: Store generated artifacts in FOTOhub S3.
query_database: Connect to managed FOTOhub PostgreSQL or Redis instances.

5. HTTP Requests

fetch_api: Construct raw HTTP GET/POST/PUT requests to interact with third-party webhooks and REST APIs.

Persistent Workspaces

Every FOTOhub user gets an isolated, persistent filesystem available to their agents.

Path: /data/workspaces/{user_id}/
Persistence: Files survive across individual agent runs. If Agent A generates a dataset on Monday, Agent B can process it on Tuesday.
Limitations: Workspace storage is billed at the EBS gp3 rate ($0.08/GB-month).

You can pass relative or absolute paths within /workspace/ to the agent, and they automatically map to your user's physical directory on the FOTOhub EBS volume.

Launching an Autonomous Task

Submit a multi-step objective with tool constraints, budget ceilings, and LLM selection:

PythonTypeScriptGocURL

python

from fotohub import FotoHub
import os

client = FotoHub(
    api_key=os.environ["FOTOHUB_API_KEY"],
    base_url="https://apis.fotohub.app/compute/v1"
)

task = client.post("/tasks/create", {
    "prompt": """
    1. Scrape the top 5 trending open-source AI repositories on GitHub today.
    2. Extract their stars, authors, primary languages, and architecture summaries.
    3. Generate a comparative Markdown report with an executive taxonomy.
    4. Save the file to /workspace/reports/ai_trends_weekly.md.
    """,
    "model": "claude-opus-4.6",
    "skills": ["web_research", "document_generation", "code_interpreter"],
    "max_steps": 25,
    "budget_limit_usd": 2.50
})

task_id = task["task_id"]
print(f"Task created: {task_id}")

typescript

import { FotoHub } from "fotohub";

const client = new FotoHub({ 
  apiKey: process.env.FOTOHUB_API_KEY!,
  baseURL: "https://apis.fotohub.app/compute/v1"
});

async function dispatchAgent() {
  const res = await client.post("/tasks/create", {
    prompt: "Investigate customer error trace in /workspace/error.log, identify bug, and run unit tests to confirm fix.",
    model: "claude-opus-4.6",
    max_steps: 20,
    budget_limit_usd: 1.50,
  });

  console.log("Agent running with Task ID:", res.data.task_id);
}

dispatchAgent();

package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"net/http"
	"os"
)

func main() {
	apiKey := os.Getenv("FOTOHUB_API_KEY")
	url := "https://apis.fotohub.app/compute/v1/tasks/create"

	payload := map[string]interface{}{
		"prompt":           "Write a concurrent web scraper in Go and save it to /workspace/scraper.go",
		"model":            "claude-opus-4.6",
		"max_steps":        15,
		"budget_limit_usd": 1.00,
	}
	body, _ := json.Marshal(payload)

	req, _ := http.NewRequest("POST", url, bytes.NewBuffer(body))
	req.Header.Set("Authorization", "Bearer "+apiKey)
	req.Header.Set("Content-Type", "application/json")

	client := &http.Client{}
	resp, err := client.Do(req)
	if err != nil {
		panic(err)
	}
	defer resp.Body.Close()

	fmt.Println("Task Launched, status:", resp.Status)
}

bash

curl -X POST https://apis.fotohub.app/compute/v1/tasks/create   -H "Authorization: Bearer $FOTOHUB_API_KEY"   -H "Content-Type: application/json"   -d '{
    "prompt": "Build a responsive React landing page for a coffee brand and save to /workspace/coffee-landing",
    "model": "claude-opus-4.6",
    "max_steps": 20,
    "budget_limit_usd": 2.00
  }'

Budget Controls & Safeguards

To prevent runaway costs or infinite reasoning loops, FOTOhub enforces two strict limitations on all agent executions:

budget_limit_usd: The absolute maximum dollar amount the agent is allowed to spend. If the cumulative token cost across all API calls in the loop exceeds this value, the orchestrator immediately halts the agent with a budget_exceeded error.
max_steps: The maximum number of tool invocation cycles. This prevents infinite loops where an agent fails to solve a problem and keeps trying the same tool repeatedly.

Strict Enforcement

Billing is done on a prepaid basis. If your overall account wallet drops below $0.00 during an execution, the agent is hard-killed regardless of your budget_limit_usd setting.

Real-Time SSE Token & Thought Streaming

Connect to GET /compute/v1/tasks/{task_id}/stream to receive real-time updates as the agent reasons, invokes tools, and generates artifacts.

FOTOhub utilizes Server-Sent Events (SSE) to push high-frequency updates, allowing you to render typing animations (via agent_delta events) and display agent thought processes seamlessly in your UI.

mermaid

sequenceDiagram
    autonumber
    participant Client as Frontend / SDK
    participant Engine as Agent Compute Server
    participant Sandbox as MicroVM Sandbox

    Client->>Engine: GET /compute/v1/tasks/{id}/stream
    Engine-->>Client: event: task_status (planning)
    Engine-->>Client: event: commentary ("Analyzing required packages...")
    Engine-->>Client: event: tool_call (sandbox_python)
    Engine->>Sandbox: Execute code
    Sandbox-->>Engine: Execution OK (duration: 142ms)
    Engine-->>Client: event: tool_result (output captured)
    Engine-->>Client: event: agent_delta ("Based on the data...")
    Engine-->>Client: event: done (Task finished successfully)

SSE Event Stream Reference

Event Name	Description	Payload Data Structure
`task_status`	Status transition update	`{"status": "planning" \| "executing_step" \| "completed"}`
`commentary`	Agent internal reasoning step	`{"thought": "Evaluating regression coefficients..."}`
`tool_call`	Agent dispatched a tool	`{"tool": "sandbox_python", "args": {"code": "..."}}`
`tool_result`	Tool output returned	`{"tool": "sandbox_python", "success": true, "output": {...}}`
`agent_delta`	Streaming token fragment	`{"content": "Here is the summary table:\n"}`
`done`	Task completed	`{"task_id": "tsk_...", "total_steps": 12, "cost_usd": 0.42}`
`error`	Failure event	`{"code": "budget_exceeded", "message": "Max budget reached"}`

Consuming SSE in TypeScript

TypeScript / Browser

typescript

const eventSource = new EventSource(
  `https://apis.fotohub.app/compute/v1/tasks/${taskId}/stream?token=${FOTOHUB_API_KEY}`
);

eventSource.addEventListener("agent_delta", (e) => {
  const data = JSON.parse(e.data);
  process.stdout.write(data.content); // Stream tokens to console
});

eventSource.addEventListener("tool_call", (e) => {
  const data = JSON.parse(e.data);
  console.log(`[Agent called tool: ${data.tool}]`);
});

eventSource.addEventListener("done", (e) => {
  const data = JSON.parse(e.data);
  console.log(`
Task completed! Total Cost: $${data.cost_usd}`);
  eventSource.close();
});

Human-in-the-Loop Controls

For sensitive operations (deploying to production, deleting files, committing financial transactions), the agent can pause and await explicit user authorization.

1. Pausing a Task

Force an agent to suspend its current loop:

bash

curl -X POST https://apis.fotohub.app/compute/v1/tasks/tsk_99a812df/pause   -H "Authorization: Bearer $FOTOHUB_API_KEY"

2. Responding to Agent Clarifications

When an agent reaches state awaiting_approval (because it encountered ambiguity or hit a secure boundary), submit your decision to unblock it:

bash

curl -X POST https://apis.fotohub.app/compute/v1/tasks/tsk_99a812df/respond   -H "Authorization: Bearer $FOTOHUB_API_KEY"   -H "Content-Type: application/json"   -d '{
    "approved": true,
    "user_feedback": "Proceed with deploying the migration to the staging database."
  }'

3. Cancelling a Task

Kill an agent execution completely:

bash

curl -X POST https://apis.fotohub.app/compute/v1/tasks/tsk_99a812df/cancel   -H "Authorization: Bearer $FOTOHUB_API_KEY"

Webhook Notifications

If you do not want to keep an SSE connection open, you can register a webhook to be called upon agent task completion or failure.

Provide the webhook_url in the task creation payload:

json

{
  "prompt": "Analyze log files...",
  "model": "gpt-4o",
  "webhook_url": "https://api.yourcompany.com/webhooks/fotohub-agent"
}

The payload sent to your server will look like this:

json

{
  "event": "task.completed",
  "task_id": "tsk_1093jf2",
  "status": "completed",
  "duration_seconds": 145,
  "cost_usd": 0.89,
  "result": {
    "summary": "Log analysis complete. Found 3 critical errors in auth module.",
    "files_created": ["/workspace/analysis.md"]
  }
}

Complete Agent Task Examples

Example 1: Web Intelligence Pipeline

An agent is instructed to research competitors, compile pricing data, and generate a competitive analysis matrix.

Model: gemini-2.0-flash
Tools Used: search_web, read_url, write_to_file
Result: Generates a 3,000-word markdown report autonomously in ~60 seconds for under $0.20.

Example 2: Multi-Step Data Science

An agent acts as a Junior Data Scientist.

Prompt: "Download the Titanic dataset from Kaggle, clean the missing age values using median imputation, train a Random Forest model, and plot the feature importance matrix as a PNG."
Tools Used: execute_python, fetch_api
Result: Code runs in the Firecracker sandbox. The agent iterates if a Pandas error occurs, eventually saving feature_importance.png to your /workspace/ volume.

Example 3: Full-Stack Code Generation

Prompt: "Create a Next.js frontend with Tailwind CSS and a Node.js Express backend. They should communicate over WebSockets. Save the structure in /workspace/chat-app/."
Tools Used: execute_bash (to run npx create-next-app), write_to_file (to write backend files).
Result: A complete, working monorepo scaffolded directly into the persistent workspace.

Example 4: Autonomous Content Pipeline

Prompt: "Read the latest RSS feed from HackerNews. Pick the top 3 stories. Write a tweet thread for each. Post them to Twitter using my API keys stored in /workspace/.env."
Tools Used: fetch_api, read_file
Result: The agent parses the environment variables, formats the tweets, and executes the HTTP requests to the Twitter API.

Integration with Agent Engine (DAG Workflows)

Autonomous agents deployed via Compute can be orchestrated into complex directed acyclic graphs (DAGs) using the Agent Engine:

Multi-Agent Swarm Consensus: Aggregate decisions from multiple autonomous agents via /v1/swarm/consensus.
Vector Memory RAG: Store and retrieve long-term state across sessions via /v1/memory/*.
Canary Deployments: Roll out agent logic changes safely with traffic routing via /v1/workflows/{id}/canary.
Dead-Letter Queue (DLQ): Automatically capture failures and replay them via /v1/incidents.
Zero-Secret OAuth Hub: Seamlessly connect agents with 14 external SaaS services (Slack, Google Workspace, Meta, Shopify, Notion, etc.).

For complete specifications, see the Agent Workflows API Reference.

Autonomous Agent Compute & Execution ​

Agent Lifecycle State Machine ​

Architecture Deep Dive: Agent-Compute Orchestrator ​

The Tool Calling Loop ​

Firecracker MicroVM Execution ​

Available Agent Models & Pricing ​

Language Models ​

Dedicated Compute Instances ​

Storage & Egress ​

Tool Categories Available to Agents ​

1. File Operations ​

2. Code Execution (Sandbox) ​

3. Web Search & Intelligence ​

4. FOTOhub Native API Integrations ​

5. HTTP Requests ​

Persistent Workspaces ​

Launching an Autonomous Task ​

Budget Controls & Safeguards ​

Real-Time SSE Token & Thought Streaming ​

SSE Event Stream Reference ​

Consuming SSE in TypeScript ​

Human-in-the-Loop Controls ​

1. Pausing a Task ​

2. Responding to Agent Clarifications ​

3. Cancelling a Task ​

Webhook Notifications ​

Complete Agent Task Examples ​

Example 1: Web Intelligence Pipeline ​

Example 2: Multi-Step Data Science ​

Example 3: Full-Stack Code Generation ​

Example 4: Autonomous Content Pipeline ​

Integration with Agent Engine (DAG Workflows) ​

Autonomous Agent Compute & Execution

Agent Lifecycle State Machine

Architecture Deep Dive: Agent-Compute Orchestrator

The Tool Calling Loop

Firecracker MicroVM Execution

Available Agent Models & Pricing

Language Models

Dedicated Compute Instances

Storage & Egress

Tool Categories Available to Agents

1. File Operations

2. Code Execution (Sandbox)

3. Web Search & Intelligence

4. FOTOhub Native API Integrations

5. HTTP Requests

Persistent Workspaces

Launching an Autonomous Task

Budget Controls & Safeguards

Real-Time SSE Token & Thought Streaming

SSE Event Stream Reference

Consuming SSE in TypeScript

Human-in-the-Loop Controls

1. Pausing a Task

2. Responding to Agent Clarifications

3. Cancelling a Task

Webhook Notifications

Complete Agent Task Examples

Example 1: Web Intelligence Pipeline

Example 2: Multi-Step Data Science

Example 3: Full-Stack Code Generation

Example 4: Autonomous Content Pipeline

Integration with Agent Engine (DAG Workflows)