AI Agent & Automation Development for Real Work
Bracket Mind is an AI agent and automation development company. We build agents that work inside your systems: they load skills, call your APIs and MCP servers, run code in throwaway Docker containers and pause for a person's approval before anything risky. Automation we built cut one client's manual data processing by 85%, saving about $120K a year.
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What can an AI agent automate for your business?
Work that follows rules but needs judgment: reading a task, gathering what it needs, filling in a form, creating records and handing anything unclear to a person. The 85% cut above came from exactly this kind of work, automated with the Claude and OpenAI APIs.
In production, an autonomous agent we built checks a work list for new tasks every minute and completes the forms on its own. On a client's AI backend, an agent given a URL writes its own scraper, runs it in a sandbox and creates records through the platform's API.
- Form completion and data entry inside your own systems
- Scraping websites and supplier files into structured records
- Questions about your documents answered with citations
- Back-office data processing, with exceptions routed to a person
- Workflow automation across your APIs, queues and databases
- Vibe-coding tools inside your product: an AI page designer that proposes a plan, waits for approval, builds the page section by section and edits only the part you select
How do you keep an autonomous agent safe?
By deciding in code what the agent may do on its own. On the AI backend we work on, write actions and every MCP tool call pause the run until a person approves on the live stream, and no answer within five minutes counts as no. Tools that reach the internet resolve hostnames first and refuse private, loopback and cloud-metadata addresses.
Code the model writes runs in a throwaway Docker container: 1 CPU, 512 MB of memory, 256 processes, a 120-second default time limit, a non-root user and no credentials inside, force-removed after each run. Our work-list agent gets one short-lived container per task, holding only a task id and a one-hour, callback-only token.
- Human approval for writes and every MCP tool call
- File and document text treated as data, never as instructions
- Caps on tool calls and tool results, so one runaway step can't burn the budget
- Every step streamed live, and every model call metered per tenant
What does an agent platform need beyond the model?
Agents your customers can extend without a deploy. Skills are markdown instructions loaded only when a task needs them, HTTP tools are defined as data, and MCP servers are registered as data with their tools discovered when each run starts. We build MCP integrations on the client side, connecting agents to the servers you already use.
Routing and cost control underneath. The backend routes each task across eight provider types, including Bedrock, Azure OpenAI, Anthropic, OpenAI and Gemini, and fails over to the next ranked model on transient errors. Every paid model call is logged with its tokens, latency and cost, and cost reports and an operations dashboard break spend down by product, user, model and task. One production lesson set our limits: a single 654,000-character MCP result cost 2.1M input tokens, so results are now capped at 24,000 characters.
Proof: systems we built
Autonomous AI AutomationProduction
Autonomous Work-List Agent
An AI agent that picks up assigned work-list tasks and completes the forms on its own — checking for new work every minute, with a person kept in the loop wherever it matters.
- One throwaway container per task
- No API keys inside the container
- Never submits an incomplete form
Founder-built · Read the case study
Multi-Tenant AI BackendProduction
Shared AI Platform Backend
One production .NET backend running the AI features of four client products — a medical platform, a community platform and two storefronts — instead of four separate AI stacks.
- One AI backend, four products
- 8 provider types, routed per task
- Risky tool calls wait for a human
Team project · Read the case study
Multi-Tenant Platform + AI LayerProduction
Community & Marketplace Platform
A community, marketplace and work-list platform used by many organizations, each with its own configuration — where members generate pages, forms and images by describing them in chat.
- AI added to a 3,500-commit codebase
- Upgraded from Angular 17 to 20
- 9 languages, per-tenant configuration
Team project · Read the case study
AI Customer Support SaaSLive product
Aidfolk
AI customer support that answers from a business's own docs. Add your files, website and help pages, and get a chat agent for your site that cites where every answer came from — set up in the browser, no code. Our own product, running in production.
- In production since October 2026
- Every answer cites its source
- 1,077 backend tests passing
Founder-built · Read the case study
Questions buyers ask
What's the difference between an AI agent and an automation?
An automation follows fixed steps; an agent decides which tool to use next based on what it finds. Most of what we ship combines both: deterministic code where the process is fixed, an agent where judgment is needed, and a person approving anything risky. That mix is what makes a system you can leave switched on.
Can an AI agent connect to our existing systems?
Yes. Agents call your APIs through tools defined as data and connect to MCP servers your team already runs, with every MCP call waiting for a person's approval. Credentials for user-defined tools are encrypted at rest and never placed inside the containers where model-written code runs.
Is it safe to let an AI agent run code?
Only inside a sandbox. Model-written Python or Node.js runs in a fresh Docker container with 1 CPU, 512 MB of memory, a process limit, a non-root user and no credentials, and the container is force-removed after each run. Network access stays on so the agent can scrape, which is why the boundary is that no credentials are inside.
Which models and frameworks do your agents use?
We build with Microsoft Agent Framework on .NET and Pydantic AI on Python, on Anthropic Claude, OpenAI, Azure OpenAI, Google Gemini and AWS Bedrock. One client backend we work on routes across eight provider types and fails over on transient errors. You aren't locked to one vendor.
How much does an AI agent cost to build and run?
The build is quoted after a scoping call, usually as a fixed price with a delivery date; ongoing tuning can be hourly or a retainer. Running cost is mostly model usage, so we meter every model call and show you what each task or customer costs. We also cap tool calls and tool results, which keeps one runaway run from becoming a surprise bill.