AI Agent Development Services
AI agent development services, built to survive contact with your business
Leverage Automated designs, builds, and runs custom AI agents — low-code on n8n where that is the right tool, in code against the Claude and OpenAI SDKs where it is not. Wired into the systems you already own, and operated in production rather than handed over as a repository.
Why most agent projects stall
An agent that works in a demo is roughly 20% of an agent that works on Monday.
The hard parts are not the prompt. They are the parts nobody films: what happens when the API times out mid-task, when the model returns something confidently wrong, when a permission changes, when the person the agent was supposed to hand off to is on holiday.
A demo has one path. Production has every path. Most agent builds fail somewhere between those two facts — not because the model was inadequate, but because nobody owned the failure paths.
We build for the failure paths first. That is slower to demo and considerably cheaper to live with.
What we actually build
Four layers, and we do all four
Low-code orchestration on n8n
When the job is moving data between systems with judgment at a few steps, a visual workflow is the right tool and a codebase is an act of self-harm. n8n is transparent, auditable, and inspectable by your team — not a black box you can never see inside.
Agents written in code
When the job needs real control flow, tool use, retries, and state, we write it — Python against the Anthropic and OpenAI SDKs. Some problems genuinely do not fit in a node graph, and pretending otherwise produces a workflow nobody can maintain.
Integration into what you already run
Microsoft 365, CRMs, line-of-business apps, and anything with an API. This is the part that quietly consumes most of a project, and it is the part 30 years of infrastructure work makes shorter. An agent that cannot reach your data is a chatbot.
Running it in production
Monitoring, logging, and the tuning that keeps an agent honest as prompts drift and APIs change — on your infrastructure, not ours. This is the difference between a delivered project and a working system, and it is where most of the value lives.
Who you actually get
A senior engineer, by name, for the whole engagement
Most firms selling AI agent development are high-volume development shops. You meet a salesperson, you get a team you did not pick, and the person who understood your problem in week one is on another account by week six.
We are the other thing. You talk to the engineer who builds it, and that does not change. Thirty years of enterprise infrastructure sits behind the AI layer — which matters mostly at the boundaries, where an agent meets identity, permissions, and systems that predate it.
We will also tell you when an agent is the wrong answer. A scheduled script that never surprises anyone is frequently the better build, and it is a cheaper thing to sell you.
Work that usually comes with it
AI readiness assessment
A fixed-scope two weeks that answers what to build first, what it will touch, and what it is worth — before anyone writes an agent.
Explore the assessment →AI workflow automation
Repetitive multi-step processes turned into reliable automation, taken from pilot into production and then operated.
Explore AI workflow automation →See the rest of the work on the solutions page.
Questions, answered honestly
AI agent development FAQ
What are AI agent development services?
Designing and building software that uses a language model to take actions rather than just produce text — reading a request, deciding what to do, calling the systems that do it, and knowing when to stop and ask a human. In practice that means tool definitions, control flow, error handling, permissions, logging, and a hand-off path. The model is a component. The engineering around it is the work.
Do you build on n8n or in code?
Both. Leverage Automated makes the choice per project rather than per vendor preference. n8n when the job is orchestration across systems with judgment at a few steps — it is faster to build, easier for your team to read, and simpler to change. Code when the agent needs real control flow, tool use, and state that a node graph would obscure. Forcing every problem into one of the two is how you end up with something nobody wants to maintain.
Do we own what you build?
Yes. The workflows, the prompts, and any code Leverage Automated writes for you are yours, and we will hand over the lot on request. We would rather you stay because the system works than because you cannot leave. Where we operate it for you, you are paying for the operating, not for hostage-taking — it runs on your accounts throughout.
Which models do you use?
Whichever fits the job, and Leverage Automated treats that as swappable by design. Claude and the OpenAI models for most work, local models where data cannot leave your environment. Because the model sits behind one seam rather than being wired through the whole system, a deprecation or a price change is a swap we handle rather than a migration you live through. The model account is yours, so you see the price change when it happens — you just do not have to re-engineer around it.
What drives the cost of an AI agent?
Four things, and a per-seat licence is not one of them. Scope, because one agent doing one job costs a fraction of a system that spans several. The number of integrations, because every system it touches brings its own authentication, its own rate limits and its own failure modes. The condition of your data, which is where most of the surprise lives. And whose environment it runs in — you hold the model and platform accounts in your own name and pay those vendors directly, so that cost never routes through Leverage Automated. We quote after a short call and deliberately do not publish a rate card, because the number moves with all four of those and with which engineers the work actually needs. Here is how we scope and quote.
How do we start without committing to a big build?
With the assessment. Two weeks, fixed scope, and it ends with a written recommendation of what to build first, what it touches, and what it is plausibly worth — including the honest answer if the answer is nothing yet. Plenty of teams take that document and act on it themselves. That is a legitimate outcome, and it is cheaper for you than discovering the same thing four months into a build.
Sources
Primary documentation for the model interfaces and the risk framework behind how we build agents. We link the vendors' own pages rather than summarising them, because the summaries go stale faster than the docs do.
Ready when you are
Tell us what the agent should do.
Book a 30-minute call. We will tell you whether an agent is the right build, what it would take, and what it would cost to run — and send a real plan within one business day.