AI Agent Development Services for software that decides the next step, then takes it.

An AI agent uses a language model to decide which steps to take toward a goal, then carries them out with tools, instead of following a fixed script. RPA and scripts follow a pre-scripted sequence, so they break the moment the interface changes or a case turns up that the script did not anticipate; an agent plans its own sequence from the current state and can adjust when something unexpected happens. That flexibility is the catch as well: the same input can produce different behavior, which is why an agent needs evaluation and guardrails where a script needs neither. One agent handles one well-defined task. Agentic AI is the wider system that coordinates several agents, tools and data sources to complete a multi-step workflow on its own.

There is no number on this page, because the four things that set it are yours rather than ours: how many systems the agent must reach and how clean that access is, how consequential its actions are (which is what sets the eval, guardrail and approval work), how much data preparation the job needs first, and whether we run it after launch. We quote against your scope once those are known. Budget for the running cost as well, because model and infrastructure usage does not stop at go-live, which is the line most agent budgets miss. Where you would rather fund a team than a project, a dedicated team is billed monthly per team member.

What decides how fast a pilot moves is on your side: how quickly the single job can be agreed and the tasks it will run against can be reached. Our own guide to AI agents for business puts numbers on what follows: a well-scoped single-process deployment with clean data can reach production in roughly 6 to 10 weeks, while multi-system deployments with significant data preparation typically run 3 to 6 months. The agent is rarely the long pole. Mapping the process it is meant to own usually is.

Yes, and an agent that cannot reach your systems is just a chatbot. MCP (Model Context Protocol) is the open standard for that reach, and building and securing the servers behind it is an engagement of its own. Where there is no clean API the agent can drive the interface instead, which is what our own does inside a browser, and that build taught us the limit of the approach: anything driven through a front end inherits that front end's habit of moving, so we take an API wherever one exists. A decade of wiring PMS, ERP, payment and booking platforms together is the part of this work that predates agents entirely.

Three things: an evaluation suite that scores the agent against real tasks, guardrails that bound what it can do when it is wrong, and access scoped so it can only touch what its job requires. On top of those, human approval for consequential actions, monitoring and cost controls, and access designed under our ISO 27001:2022 and ISO 9001:2015 certifications, audited by Quay Audit UK. We do not hold SOC 2, and we will tell you that before you ask.

AWS Bedrock and LangChain for the runtime, with Python and browser-automation libraries underneath. That is the stack behind our own internal agent, so the recommendation comes from operating one rather than from a partner brochure. Model choice is an engineering decision we make per case: we publish where an Anthropic Claude model is the right choice and where it is not, and the same question gets asked of every other model on the shortlist.

Any full-service firm can list the agent, the evals and the access layer, and this page does exactly that, so the useful questions all sit one level below the category. Three of them are answerable from outside the room, and we should have to answer them too. Ask what the vendor has run rather than demonstrated, and ask for the failure modes: a demo is a curated path, and ours are published in the case study for our own agent, down to multi-factor sign-in and the front-end selectors the whole thing depended on. Ask who employs the engineers, because in month six, when a prompt needs retuning, that decides whether the person who wrote it can still be reached; ours are unicrew employees across six countries, and the engineering happens in Ukraine, Poland, Estonia and the UK. Then ask for review URLs instead of a rating: the number is a summary somebody else computed, and the interview underneath it is where the useful detail sits. Every client quoted here links to theirs on Clutch.

The agents we have run end to end are our own. One works a browser-based admin job across unicrew's own team and ran as a proof of concept; two AI products we own and sell run in production with active users. For clients we have built the AI layer inside their products, including the tagging and embeddings behind an EdTech platform's search, and the automation and integration work an agent depends on to reach anything. What we cannot show you yet is a client agent with a published outcome. Ask any vendor, us included, what its agents do unattended today and for how long, and compare the answers rather than the logos.

When the job sits entirely inside one product you already pay for, because that vendor's own agent will be cheaper than anything we could write. When the data it would need is not reachable without a data engineering project you have not funded. When the process still changes every month, or nobody can state what a correct outcome looks like. Also when the job is genuinely about judgment, relationships or organizational context: an agent can surface data and draft options there, but it should not own the workflow. A general-purpose assistant is the other common trap, and the most reliable way to end up with a pilot that never gets promoted.