Build
The Agent Work We Do
What each part of the build involves.
Services / Software Development
AI Agent Development
Services
Service portfolio
Agents That Do Work, Inside Limits You Set
An AI agent completes a task across several steps, deciding what to do next and calling your tools and APIs as it goes. We set what it may do unsupervised, what needs a person's approval, and what gets recorded. Most agent failures are design failures rather than model failures: unbounded loops, no way to inspect a decision, and no limit on what a tool call can reach.
Build
Scoping And Agent Design
Deciding what the agent handles, what it hands to a person, and what it never touches. We start with one task that has a clear success condition, not a general assistant.
Mobilise
Tool And System Access
Connecting the agent to your APIs, databases and SaaS platforms, through MCP servers or direct integration. Each tool gets its own permission scope, so read access does not quietly become write access.
Experience
Orchestration And Memory
A single agent with tools by default, because it costs less to run and is far easier to debug. Multiple agents only where one genuinely cannot cope. Memory scoped so old context does not pollute new decisions.
Connect
Guardrails And Human Approval
Action limits, spend caps, loop protection, and approval steps before anything irreversible. Prompt injection and data exfiltration get tested for, not assumed away.
Operate
Evaluation
Agents are scored on the whole path, not the final answer: which tools it chose, in what order, and whether the task actually completed. Every production failure becomes a regression test.
Operate
Deployment And Monitoring
Tracing on every run through OpenTelemetry, with dashboards for task success, token spend and cost per completed task. You can see what the agent did, and why.
Ways We Bring These Teams Together
The Engagement Models We Offer
Six ways to work with us.
Agile Pods
AI and QA engineers in two- to four-week sprints. Suits one agent with a defined scope and an end date.
Product Development
A full-cycle team taking an agent from scoping through to production, including evaluation and monitoring.
Dedicated Remote Centres
Long-term AI engineers working only on your roadmap. Webmyne handles recruitment, payroll, legal and HR.
On-Demand Staffing
One or two specialists for a fixed period, such as an engineer to build an evaluation suite.
Fixed Price
Scope and budget agreed before work starts. Suits a single agent over documented tools, less so open-ended autonomy.
Support & Optimisation
Ongoing evaluation, prompt and tool updates, and cost tuning. Agents drift as your systems and data change around them.
Our Process
The five stages every project runs through.
Discover
The task, the systems it touches, and what a wrong action would cost.
Define
Permission scopes, approval points, the success condition, and where the agent escalates when unsure.
Build
Tool integration and orchestration in sprints, tested in a sandbox before touching production data.
Test
Outcome and trajectory scoring, adversarial testing for prompt injection, and load testing against rate limits.
Launch & support
Staged rollout on a narrow scope first, with tracing and alerting, then support under an agreed response time.
Ready To Hand Over A Task?
Tell us one task you would give an agent, and what it must never do without asking. We will tell you whether it is a good first candidate.
Plan your projectFAQs
If the steps are always the same, a scripted workflow is cheaper, faster and easier to audit. An agent earns its cost when the path varies with the input, and a person would otherwise decide what happens next.
Permission scopes limiting what each tool can reach, approval steps before irreversible actions, spend and loop caps, and sandbox testing for anything that writes to production. Actions outside the allowed scope are tested for explicitly.
By scoring the whole path rather than the output alone: which tools it chose, in what order, and whether the task completed. Tests run against fixed cases before release and sampled traffic after.
A single agent over documented tools typically runs six to ten weeks to a supervised pilot. Widening its scope or removing approval steps takes longer, because that is where the evaluation and governance work sits.
Ongoing spend is token usage plus infrastructure, and it scales with how many steps each task takes. We report cost per completed task rather than per call, because that is the number that decides whether the agent is worth keeping.