Most 'AI' a business buys can only talk. An agentic AI app can act — it reads your systems, decides what to do, uses real tools to do it, and writes back a plain-English record of what changed. From $4,997, running in three weeks, and the code is yours.
Your agent inherits 1,640 tools across 111 services on day one, because we are not writing integrations from scratch — we are pointing an agent at ones already running in production. Approval gates stay on anything irreversible until you take them off.
It was never allowed to touch anything.
A company buys AI, wires it to a knowledge base, and gets a system that answers questions about the work. The work itself is still done by a person, who now also reads AI summaries. That is a net increase in labour dressed up as automation.
The gap is not model quality. Models have been good enough for a while. The gap is permission and plumbing — whether the AI can reach your CRM, your calendar, your billing, and your inbox, and whether anyone trusted it enough to let it write instead of read.
That is the entire job of an agentic build: give the model real tools, real boundaries, and a record of what it did — so the task finishes without a human in the middle of it.
What makes it agentic
Six things that separate an agent from a chat window.
Agents that act, not answer
A chatbot returns text. An agent books the appointment, moves the deal stage, sends the invoice, updates the sheet, and tells you it did. The difference is tool access — and tool access is the whole product.
111 services already wired
Your agent inherits 1,640 tools on day one — CRM, Stripe, Slack, Google Workspace, Shopify, QuickBooks, Supabase, Twilio and more. We are not writing integrations from scratch; we are pointing an agent at ones that already run in production.
Orchestration, not a single prompt
Real work is multi-step and conditional. Agents run in pipelines, hand off to each other, retry, and escalate to a human when confidence drops. You define the outcome; the orchestrator decides the path.
A visible audit trail
Every run is logged in plain English: what was asked, what the agent did, which tools it called, what changed. Non-technical staff can read it. That is what makes an agent safe to leave running.
Credentials never touch the browser
Keys live server-side, per-tenant, encrypted. The front end carries a public key that can only do what you allow it to do. Revenue and PII endpoints require a separate secret.
You own the code
The repo is yours at handoff — source, infrastructure config, and docs. No per-seat licence on your own application, and no vendor who can switch you off.
What you actually receive
Not a demo and a slide deck. A working agent, the code behind it, and enough documentation that your team can change it without calling us.
Build my quote- Discovery: the outcome written down, plus the decision rules the agent must respect
- The agent itself — prompt architecture, tool scope, guardrails, escalation path
- 0nMCP wiring to every system it needs to touch (CRM, billing, comms, storage)
- A human-readable run log so anyone on your team can see what it did
- Approval gates on anything irreversible — money, sends, deletions
- Deployment to your infrastructure (or ours) with monitoring and error alerting
- Handoff: the repo, the credentials, the docs, and a walkthrough recording
- 30 days of post-launch tuning as real usage exposes real edge cases
A typical AI build vs an agentic build
Compared on structure and scope rather than benchmarks — we do not have a sourced industry study on agentic build costs, so we are not going to invent one.
* Structural comparison of what is in scope and who owns what. The 'typical' column describes common practice we encounter in discovery, not a published benchmark.
Three weeks, start to supervised production
Five steps. You see the tool map before we write a line of agent code.
Day 1–2
Name the outcome
We do not start from 'we want AI'. We start from a sentence like 'every inbound lead gets qualified, tagged, and booked without anyone touching it'. Then we write down the rules the agent may never break.
Day 3–4
Map the tools
We list every system the outcome touches and confirm which of the 111 services already cover them. Anything missing gets built as a new tool. You see the map before we write the agent.
Week 2
Build the agent
Prompt architecture, tool scope, guardrails, escalation path, and the run log. We build against real data from your systems, not a demo fixture, so edge cases surface now instead of in production.
Week 3
Supervised run
The agent runs on live work with approval gates on anything irreversible. You watch the log, approve or reject, and we tune. Gates come off one at a time, only once you are comfortable.
Week 3 + 30 days
Handoff and tune
Repo, credentials, docs, and a walkthrough recording are yours. We stay on for 30 days of tuning while real usage teaches the agent what discovery could not.
Agentic AI Pricing
One-time builds, not seats. You own what we hand over.
Agent Pilot
one-time · 3 weeks
One agent, one outcome, running in production.
Proving the model on a single high-friction process before you commit further.
- 1 agent, scoped to 1 outcome
- Up to 5 connected systems
- Plain-English run log
- Approval gates on irreversible actions
- Deployment + monitoring
- Repo handoff — you own it
- 30 days post-launch tuning
Agent Suite
one-time · 5 weeks
Several agents that hand work to each other.
A whole process — intake through fulfilment — rather than a single step.
- Everything in Pilot, plus —
- Up to 4 agents with orchestration between them
- Unlimited connected systems across all 111 services
- Shared memory and context between agents
- Role-based approval routing
- Admin dashboard for the run log
- Custom tools for anything not already covered
Embedded
retainer
Agents as an ongoing capability, not a project.
Teams shipping new agents continuously, or running them at volume.
- Everything in Suite, plus —
- New agents on a standing retainer
- Private 0nMCP tools for your proprietary systems
- Multi-tenant architecture if you resell to your own clients
- SLA + priority response
- Direct line into the 0nMCP roadmap
Every build includes the repo at handoff, approval gates on irreversible actions, and 30 days of post-launch tuning. Hosting and model usage are billed at cost or run on your own accounts — your choice, decided during scoping.
Questions people actually ask
What is an agentic AI app, and how is it different from a chatbot?
A chatbot produces text. An agentic AI app produces outcomes — it has access to real tools and uses them. Ask a chatbot to reschedule a client and it writes you a polite paragraph about how to do that. Ask an agent and it checks the calendar, moves the appointment, sends the confirmation text, updates the CRM record, and reports back what it did. The technical difference is tool access plus an orchestration loop that lets the AI decide which tools to call and in what order. The practical difference is whether a human still has to do the work afterwards.
How much does a custom agentic AI application cost?
A single-outcome agent starts at $4,997 as a one-time build, live in about 3 weeks. A multi-agent suite that covers an entire process — intake through fulfilment, with agents handing work between them — is $9,997 over roughly 5 weeks. Ongoing agent development runs as a custom retainer. The reason these numbers are lower than a typical ground-up build is that the integration layer already exists: 0nMCP ships 1,640 tools across 111 services, so we are configuring and orchestrating proven integrations rather than writing connectors from scratch.
Which systems can the agent actually connect to?
0nMCP covers 111 services and 1,640 tools across 22 categories, including CRM, Stripe, Slack, Google Workspace (Gmail, Calendar, Sheets, Drive), Shopify, QuickBooks, Supabase, Twilio, Notion, Airtable, GitHub, HubSpot and more. If your system is not in the catalogue, we build it as a new tool during the mapping phase — it joins the same catalogue and works like the rest. You see the full map of what is covered before we start building the agent.
How do you stop an AI agent from doing something destructive?
Three layers. First, tool scope: the agent is only given the tools it needs, so it cannot call what it was never handed. Second, approval gates on anything irreversible — money moving, messages sending, records deleting — which hold the action until a human approves it. Third, escalation on low confidence: when the agent is unsure, it stops and asks rather than guessing. During the supervised run in week 3 every gate is on, and they come off one at a time only when you are comfortable. Credentials stay server-side and never reach the browser.
Do we own the agent, or are we renting a platform seat?
You own it. At handoff you receive the repository, the infrastructure configuration, the documentation, and a walkthrough recording. There is no per-seat licence on your own application and no vendor who can switch it off. 0nMCP is the orchestration layer underneath — the agent you paid for is yours.
What happens if the AI model we started on gets replaced?
Model choice is a configuration value, not an architectural commitment. The agent's logic lives in its tool definitions, guardrails, and orchestration — none of which are tied to a specific vendor's API. Swapping the underlying model is a config change, not a rewrite. This matters more than it sounds: the model landscape has turned over repeatedly, and anything hard-wired to one vendor gets rebuilt every time it does.
How long before the agent is doing real work?
Three weeks from kickoff for a single-outcome agent. Days 1 to 2 define the outcome and the rules, days 3 to 4 map the tools, week 2 builds the agent against your real data, and week 3 runs it supervised on live work with approval gates on. Most clients see it complete a real task correctly in week 2 — the third week is about trusting it enough to take the gates off.
Tell us the outcome. We will tell you if an agent can do it.
Some processes should not be handed to an agent, and we will say so — a scoping call that ends in “automate this the boring way instead” has saved people more than one that ends in a build.
30 minutes from quote to kickoff
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