AI Agent Development
Multi-step AI systems that use tools and data to complete tasks, with clear limits on what they may do.
What is an AI agent?
An AI agent is a system where a language model plans and carries out a multi-step task using tools — searching data, calling APIs, writing records — rather than just answering a single prompt. Agents suit tasks with variable steps. They need explicit boundaries on what they may access and change, because an agent acting autonomously can cause real damage.
Why this matters
Agents are useful when a task varies enough that a fixed script cannot cover it, but the tools required are well defined. They are risky when given broad permissions, because a wrong decision becomes a wrong action rather than a wrong sentence. Scope and permissions are the design work.
Where agents fit, and where they do not
- Suitable: research and gathering across several systems, where the path varies by case.
- Suitable: triage that needs to look things up before deciding what to do.
- Unsuitable: fixed, repeatable sequences, where ordinary automation is cheaper and more reliable.
- Unsuitable: irreversible actions such as payments or deletions without human approval.
How we build agents
Define the tools narrowly and give each one the minimum permission it needs. Keep read and write capabilities separate, and require approval for anything irreversible. Log every step so behaviour can be reconstructed. Bound iterations and cost per task. Test against realistic cases, including deliberately awkward ones.
What ai agents includes
Every engagement is scoped to your situation, but these are the components the work is built from.
- Tool definitionA narrow, explicit set of capabilities rather than broad system access.
- Permission boundariesRead and write separated, with approval gates on irreversible actions.
- Step loggingA complete record of the agent's reasoning path and actions for review.
- Cost and iteration limitsHard bounds so a stuck agent cannot run up cost indefinitely.
- Evaluation suiteRealistic test cases including edge cases and adversarial input.
- Human handoverDefined escalation when confidence is low or the case is out of scope.
What changes for your business
- Multi-step tasks handled without a person coordinating each step.
- Explicit limits on what the system can do.
- A reviewable record of every action.
- Predictable cost per task.
Technology and platforms
Chosen to match how the work will actually be maintained, not by preference.
How the work runs
Each stage ends with something you can review, so course corrections happen early rather than at handover.
- Find the taskA repetitive, language-heavy process with a measurable current cost in hours.
- BaselineTime and error-rate measured before anything changes, so the benefit is provable.
- Build smallThe smallest working version, with a human review step where errors would matter.
- EvaluateTest against real cases, including awkward ones, and compare to the baseline.
- Cost and scaleModel running cost at real volume, then extend to the next process.
Industries we apply ai agents in
The technical work is often similar across sectors. What differs is what customers need to see before they act.
What drives the cost
We publish what moves the price rather than a headline figure, because the figure without the drivers is not comparable.
- Number of tools and integrations
- Permission and approval complexity
- Evaluation coverage
- Expected task volume
Why work with SWT Company
Scoped before it is quoted
We ask what the business needs before proposing work. A quote given before that conversation is a guess.
Measurement first
Tracking — including calls and WhatsApp — is verified before spend, because every later decision depends on it.
No claims we cannot support
No ranking guarantees, no invented results, no numbers we cannot evidence.
You own everything
Domain, hosting, repository, analytics and ad accounts stay in your name.
Built to be handed over
Documentation and clear structure, so you are never dependent on one supplier.
Mobile-first in practice
Designed at phone width and tested on mid-range devices over mobile data.
Projects and case studies
We publish case studies only where a client has agreed and the results can be evidenced. None are published yet, because no verified client data was supplied for this build and inventing results is not something we will do. Ask and we will arrange references in your sector directly.
AI Agents — frequently asked questions
If the steps are the same every time, ordinary automation is cheaper, faster and more reliable. Agents earn their place when the path genuinely varies by case.
Yes, within explicitly granted tools. Irreversible actions should require approval. Broad write access to production systems is not a good idea however capable the model is.
Narrow tools, least-privilege permissions, iteration and cost limits, approval gates, full logging, and evaluation against awkward cases before release.
Less reliable than deterministic code and improving. Treat them as capable assistants requiring supervision, not as unattended staff.
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Talk to us about ai agents
Call or message for anything urgent. Otherwise the form reaches the same place.
Get a quote for ai agents
A short conversation is usually enough to establish scope, timeline and a realistic range.