Autonomous AI Agent Platform for Business Automation
An enterprise platform where AI agents write and execute their own code inside sandboxed containers, with 70+ integrated tools and a complete audit trail of every action.
The Problem
Organizations under pressure to automate knowledge work keep hitting the same wall: most AI tools are conversational assistants that cannot actually execute anything. Connecting a model to real enterprise systems—email, databases, cloud storage, accounting software—means extensive custom development for every integration, and even then the result needs constant human supervision.
The deeper blockers are trust and control. Without sandboxing, an agent that misbehaves can corrupt data or take unsafe actions against live systems. Without audit trails, there is no way to debug what an agent did or prove compliance. And without cost controls, autonomous execution can quietly burn through LLM and compute budgets. These concerns, more than model capability, are what keep AI automation stuck in pilot mode.
What We Built
We built a production platform where agents are goal-oriented executors rather than chat assistants: given an objective, an agent writes its own Go code, which is compiled dynamically and run inside an isolated Docker container with resource limits, streaming its output live over WebSocket. Along the way it can draw on an ecosystem of 70+ pre-built tools—Gmail, document OCR, forgery detection, GitHub automation, spreadsheet operations, stock chart generation with 50+ technical indicators, and more.
Safety and accountability are architectural, not bolted on. A static linter parses generated code and blocks dangerous operations before execution, a PyTorch-based World Model service watches execution patterns for behavioral anomalies, and every agent action lands in an append-only, versioned PostgreSQL record. Multi-LLM orchestration spans OpenAI, Anthropic Claude, Google Vertex AI, and Groq with automatic provider failover, and Stripe-based usage billing tracks per-execution cost across LLM, compute, and storage.
Code-writing agents in sandboxed execution
Agents generate Go code to solve their objective, which is compiled and run in per-agent Docker containers with resource limits and live stdout/stderr streaming—real execution without exposing live systems.
70+ production-ready tools
A tool ecosystem covering email, OCR and forgery detection, GitHub repositories, spreadsheets, and market analysis with 50+ technical indicators, so agents plug into real workflows without custom integration work.
Multi-LLM orchestration with failover
Supports OpenAI, Anthropic Claude, Google Vertex AI, and Groq behind one interface, with automatic provider failover so a single vendor outage never halts running automations.
Layered safety and auditability
A Go AST linter blocks unsafe operations before execution, a PyTorch World Model flags behavioral anomalies during it, and an append-only versioned data model preserves the full history of every action after it.
How It Works in Practice
A finance team automating invoice intake—work that previously meant a person reading each email, checking the attachment, and keying data into a spreadsheet.
- 1
Receive the objective
The agent is pointed at an inbox with a standing objective: process incoming invoices. It plans the task and writes the Go code needed to execute it.
- 2
Execute in isolation
The generated code runs inside a sandboxed Docker container, calling the Gmail tool to fetch attachments and the OCR and forgery-detection tools to extract and verify invoice data.
- 3
Guard the run
Before execution the linter screens the code for dangerous operations; during execution the World Model service checks the behavior pattern against expectations.
- 4
Record and bill
Extracted data lands in the target spreadsheet, every action is written to the versioned audit trail, and the run cost is metered across LLM, compute, and storage for usage-based billing.
Screenshots
Engineering Approach
Agents that execute, not converse
Instead of chaining prompts, agents produce real compiled code with real tool calls—which makes their behavior testable, auditable, and reliable enough to run without a human watching.
Safety as architecture
Sandboxing, static analysis, anomaly detection, and AES-256 field encryption are structural layers of the platform, so adding a new tool or agent inherits the safety posture rather than re-implementing it.
An economy around every execution
Per-execution cost tracking across LLM, compute, and storage feeds Stripe usage billing directly, keeping the unit economics of automation visible instead of buried in a monthly cloud bill.
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