AI Agent Development
AI Agent Development Company
We design, build, and operate production AI agents — SDR and acquisition agents that take real actions, not chatbots wearing an "agent" label. A separate fleet, a marketing department with its own provisioning layer, runs this agency day to day.
An AI agent development company designs, builds, and operates AI agents: software that reads inputs against defined criteria, decides what happens next, and takes the action — not a chatbot that just answers in one turn. Retailbox builds SDR and acquisition agents for B2B clients; a separate marketing-agent fleet runs the agency itself.
What we build
Two kinds of agents cover most of what B2B teams actually need — each one scoped to a narrow, well-defined job rather than a whole role:
SDR & outbound agents
Agents that research a prospect, personalize outreach, and manage the follow-up sequence — freeing your sales team to work only the replies that need a human.
Acquisition & marketing agents
Agents that run parts of your acquisition pipeline end-to-end — sourcing, first-pass copy, scheduling — with a human checkpoint before anything goes out.
Agents we've shipped
Every claim below traces to a specific build — not a general promise about what AI agents can do.
AI acquisition-engine agent for a marketing client
A standing agent that runs acquisition-pipeline steps for a client's marketing operation — built, deployed, and maintained by our team.
AI SDR agent for a B2B client
An outbound SDR agent that qualifies and sequences leads for a client's sales pipeline, handing off only the conversations that need a person.
Our own agent fleet
We don't only build agents for clients — we run them inside our own agency, every day:
- An agent-provisioning and operations layer that manages the rest of the fleet — accounts, budgets, wake schedules, guardrails.
- A department of marketing agents — SEO, content, and reporting — that plans and ships its own work inside an approval queue we review.
How Do You Build a Production AI Agent?
1 — Scope the agent's job
One job, defined narrowly: what it decides, what it's never allowed to decide, and what "done" looks like. Most failed agent projects skipped this step and tried to automate a whole role at once.
2 — Build with a human checkpoint
We ship the agent behind a review gate first — you see every decision it would have made before it makes one unsupervised. The gate loosens only where the track record earns it.
3 — Operate it like a system, not a demo
Logging, error handling, and a fallback for when the model or an API changes under you. An agent that quietly stops working is worse than one that never shipped.
Is an AI agent the right fit for your task?
Not every repetitive task needs a custom agent — here's the honest split we use on the first call:
A single-tool task with no real cost if it's wrong
Skip the agent. A prompt in ChatGPT or Claude, used by a person, is faster to set up and easier to fix.
A repeatable decision that currently eats hours and has clear criteria
Good agent candidate — outbound sequencing, follow-up drafting, first-pass campaign copy. This is where the payback is fastest and easiest to measure.
A judgment call with real downside if the agent gets it wrong
Build it with a mandatory human approval step, not full autonomy. We scope where the line sits before writing any code.
What it costs — real numbers
Priced by what the agent has to do, not by the word "agent" on the invoice:
Single-purpose agent
$2,000–$5,000
1–2 weeks
One narrow job — outbound sequencing, follow-up drafting, first-pass campaign copy — wired into the tool you already use.
Multi-step agent system
$15,000–$75,000
6–16 weeks
Retrieval over your documents, CRM actions, multi-agent handoffs, and guardrails built to run unsupervised.
Managed operation
from $299/mo
Month-to-month
Monitoring, logging review, and free fixes when a model or API changes under the agent. Cancel anytime.
Every engagement starts with a free 30-minute call that scopes the agent's job before any price is fixed. Want the AI wired into an existing tool instead of a standalone agent? See AI integration services, or if you need a conversational front end specifically, see AI chatbot development services.
Who builds it
Michael Oskola — founder of Retailbox. He scopes every agent engagement personally; implementation runs with a small vetted team he's worked with for 5+ years. 400+ projects since 2017, Top 1% ranked on Upwork.
Based in Orlando, Florida — working with B2B teams across the US. Need the fuller build — CRM, workflows, and agents together? See the AI automation agency track, or browse the full services catalog.
Questions we get asked
What makes a company an "AI agent development company" rather than just a chatbot builder?+
An agent takes actions and makes decisions across steps — not just answers a question in one turn. We build agents that read inputs against real criteria, decide what happens next, and take the action (route a lead, draft a follow-up, flag an exception) — with a human checkpoint where the decision has real downside. We also operate our own fleet of agents inside our agency.
How much does custom AI agent development cost?+
A single-purpose agent — outbound sequencing, follow-up drafting, first-pass campaign copy — runs $2,000–$5,000 and ships in 1–2 weeks. A multi-step agent system with retrieval, CRM actions, and guardrails runs $15,000–$75,000 over 6–16 weeks. Every engagement starts with a free 30-minute call that scopes the job before any price is fixed.
How do you keep an agent from making a costly mistake unsupervised?+
We scope exactly what the agent is allowed to decide on its own versus what needs a human, before writing code. New agents ship behind a review gate — you see what it would have done before it acts unsupervised — and the gate only loosens where the track record earns it.
Can you build an agent that plugs into the CRM or tools we already use?+
Yes — agents are wired into your existing stack via API, webhook, or MCP rather than requiring you to switch tools. What matters more than the integration method is scoping the agent's job narrowly first; the wiring is the easy part.
What happens when the underlying AI model changes or gets deprecated?+
The agent is built to keep running through model and API changes, with monitoring and free fixes under a managed plan — that failure mode (an agent quietly stops working and nobody notices) is exactly what the managed plan exists to catch.
Do you actually use AI agents in your own business, or just build them for clients?+
Both. A small internal department of agents plans and drafts its own marketing work inside an approval queue we review, and a separate provisioning layer manages the rest of that fleet — accounts, budgets, wake schedules, guardrails.
Scope your agent on a free call
30 minutes. You leave knowing whether the task needs a custom agent at all, and what it should cost if it does.