Agents that act. Not just answer.
A digital worker takes the first pass on real work the moment it lands - planning, acting in your tools, and checking itself - and pulls in a human only on the calls that need one.
The capability, defined.
This is where the ladder stops answering and starts doing. A chatbot returns text; an autonomous agent is a digital worker - you hand it a goal and a set of constraints, give it real tools (your CRM, your inbox, your APIs over MCP), and it decides the path at runtime, acts, checks its own work, and loops until the job is done. The model underneath is fungible and getting cheaper; the durable thing we build is the system of work around it - the tools, the guardrails, the evals - that turns a clever demo into an employee that shows up every day.
Not a chatbot. Not a copilot that suggests while your team still does the work. It's an autonomous agent given a goal and real tools over MCP - it decides the path at runtime, acts in your systems, evaluates its own output, and loops until the job is actually done.
What this costs you today.
A person runs the same multi-step process all day, and the 'AI' you bought just makes suggestions they still have to execute.
The anatomy of the system.
The model underneath is fungible and getting cheaper. The durable thing we build is the system of work around it - and that system is what turns a clever demo into an employee that shows up every day.
Engineered, not prompted.
We follow Anthropic's own rule - start with the simplest thing that works, and only hand control to an agent where the path genuinely has to be decided at runtime - built on Claude Code, the Claude Agent SDK, n8n, Railway, Vercel, Cloudflare, and Supabase.
What this looks like in the wild.
The reliability that ships.
The task-accuracy jump Anthropic measured on Opus 4 when MCP tool-search loads only the tools a step needs - evidence that the engineering around the model, not the model alone, drives reliability.
The reduction in tool-definition token usage from progressive tool-loading - the kind of cost engineering that keeps an autonomous agent from running up a surprise bill.
Active public MCP servers as of late 2025 - the tool ecosystem your agent plugs into is now standard infrastructure, not a bespoke integration per vendor.
↳ Industry benchmarks and engineering standards, not Anfloy client metrics - we report your real numbers once you're live.
Named tools, and why.
The model is fungible - the system is the moat. Here's what we build it on, and the reason each earns its place.
The honest fit check.
Teams with a high-volume, multi-step process that today eats a person's whole day - sales development, support, research, ops - who want a digital worker that completes the task in their real tools and ships into their repo.
If the path is fully knowable in advance, you don't need an agent - you need a deterministic workflow, which is cheaper and more reliable, and we'll build that instead. And if the work tolerates zero autonomy and every action needs human sign-off, the agent overhead won't pay for itself yet.
The honest answers.
Aren't autonomous agents unreliable?
An ungoverned agent is - a well-engineered one isn't, and the difference is exactly the engineering most people skip. We constrain the agent to a clear goal, give it a self-evaluation loop so it checks its own work, gate irreversible actions behind approvals, and back it with an eval suite that proves task completion before you trust it. We also tell you honestly when a problem should be a deterministic workflow instead - that's usually the more reliable answer, and it's the exact line Anthropic draws between a workflow and an agent.
How is this different from a chatbot?
A chatbot answers questions - it waits to be prompted and returns text. An autonomous agent is given a goal and real tools over MCP: it plans its own steps, acts in your CRM, inbox, and APIs, evaluates the result, and loops until the work is done. The shift is from a copilot that suggests to a digital worker that completes the task. You're not selling your team a new tool to learn - you're getting the work done.
Do we own the agent, or are we renting it from you?
You own it. The agent code, the MCP tool layer, and the evals all ship into your repository and run on your accounts, your keys, your infrastructure. Built once, yours forever - it keeps running with or without us, with no Anfloy platform to be locked into. Because MCP is an open standard and the model underneath is interchangeable, you're never hostage to one vendor's framework or pricing either.
What happens when it breaks or makes a bad call?
Failure is designed for, not hoped against. Irreversible actions sit behind approval gates, the self-eval loop catches low-confidence output and retries or escalates, and every plan and tool call is traced so you can see exactly what happened. If the agent gets stuck, it hits its loop and budget caps and hands off to a human cleanly instead of spinning. We monitor it in production and tune from the traces - a bad pattern gets caught on evals and fixed, not left to recur.
How long does an agent take to ship?
A focused agent typically ships in a few weeks - we start with the narrowest version that completes one real task end to end, prove it on an eval suite, then expand its tools and autonomy from there. We deliberately ship a workflow-first version where the path is known and only hand control to the autonomous loop where it's earned, so you get reliable value fast rather than waiting on a moonshot.
Does it run on our infrastructure, and how do you control the cost?
It runs on your accounts - containerized on Railway, Modal, or your own cloud, under your keys. Cost control is part of the architecture: we set token and spend budgets per run, cap loop iterations, keep cheap deterministic steps out of the model, and use MCP tool-search and code execution so the agent isn't burning context on every call - Anthropic's own numbers show cutting tool-definition tokens ~85% and context overhead dramatically. You get tracing on what each run costs and why, so spend is observable and tuned, never a surprise.