Agents go beyond a chat window: they call your systems, follow your rules and hand back to a person when unsure. We design them to be measurable, controllable and maintainable.
What this helps you achieve
Variable work handled
Agents read context, choose tools and complete tasks that rigid automation cannot.
Measured quality
Evaluation sets track accuracy so improvements and regressions are visible.
Bounded cost and risk
Permissions, spending limits and approval steps keep agents inside safe limits.
Auditable actions
Every tool call and decision is logged for review.
About this service
An AI agent is software that can pursue a goal using several steps and tools. Instead of answering one question, it can look up information, call your systems, decide what to do next and report back.
Agents are most useful for work that varies from case to case, such as researching a lead, triaging a support request or preparing a draft for review. We give each agent a narrow job, only the tools it needs and clear limits. Actions that change data or contact customers require approval until the agent has proven reliable, and every step is logged so you can see how it reached a result.
1.Goal
A clear task with limits and success criteria.
2.Plan
The agent decides which steps and tools to use.
3.Use tools
It calls search, databases and APIs you have allowed.
4.Check
Outputs are validated against rules and sources.
5.Approve
A person confirms sensitive actions.
6.Report & log
Results and every step are recorded.
Does this sound familiar?
Work that depends on reading context and making judgement calls is hard to automate with simple if-then logic.
Answers live in documents, inboxes and tools, and people spend hours gathering context.
Preparing briefs, enriching records and summarising material consumes skilled time.
Hallucinations, runaway actions and unpredictable cost make teams cautious, rightly.
What's included
Agents call your APIs, databases and SaaS tools through narrowly scoped functions.
Ground answers in your documents and data, with sources, and keep relevant context between steps.
Allow-lists, input and output validation, and least-privilege credentials.
Test sets built from real examples so quality is measured, not assumed.
Traces, logs and cost tracking for every run.
Clear handoff to a person when confidence is low or an action is sensitive.
How it works
We write down what the agent should do, which tools it needs and what 'good' looks like.
A working prototype is tested against real examples before we invest further.
Add guardrails, permissions, error handling and approval steps.
Ship, watch real usage and tune using logs and evaluation results.
Business benefits
Agents cope with cases that do not fit a fixed rule, then hand over when they are unsure.
Each agent receives only the permissions and tools its task requires.
Approval steps, spending limits and escalation paths keep humans in the loop.
Logs show the tools called and the information used for each outcome.
We test agents against real examples and adjust before widening their scope.
Practical examples
Illustrative scenarios that show how we approach typical problems. They are examples, not descriptions of specific clients or guaranteed results.
Tools & technologies
We choose tools to fit your systems and team. Links go to each tool's official documentation.
Claude API
Language models with strong long-document and tool-use support.
Official documentation for Claude API (opens in a new tab)OpenAI API
Language models for classification, extraction and drafting.
Official documentation for OpenAI API (opens in a new tab)Model Context Protocol
An open standard for giving AI applications safe access to tools and data.
Official documentation for Model Context Protocol (opens in a new tab)Node.js
Back-end services, APIs and background jobs.
Python
Data processing and AI workloads.
Official documentation for Python (opens in a new tab)TypeScript
Typed code that catches mistakes before they reach production.
Official documentation for TypeScript (opens in a new tab)PostgreSQL
Relational database for structured business data.
Official documentation for PostgreSQL (opens in a new tab)pgvector
Vector search inside PostgreSQL for retrieval-augmented answers.
Official documentation for pgvector (opens in a new tab)Use cases
Examples of where businesses typically begin. Your best first project depends on your workflow.
Researches prospects and prepares briefs and draft outreach for your review.
Reads tickets, looks up account data and drafts accurate replies.
Answers staff questions and performs routine admin tasks across tools.
Gathers company details and scores leads against your criteria.
Checks documents against checklists and flags exceptions.
Queries your data, produces summaries and explains what changed.
Related work
Built products and clearly labelled illustrative examples related to this service.
Illustrative example · AI agents
An agent that triages requests, drafts replies and escalates.
Developer tools
An interactive tool that helps developers compare AI coding agents against the criteria that matter for their own workflow.
FAQ
An AI agent is software that uses a language model to decide what to do next, calls tools such as your CRM, database or email, and works through a multi-step task. Unlike a simple chatbot it can take actions, so it needs permissions, logging and limits.
An automation follows fixed steps. A chatbot answers questions. An agent chooses its steps based on context and can use tools to act. Many solutions combine all three, and we recommend the simplest option that solves the problem.
By narrowing what it can do, validating its inputs and outputs, testing against real examples, logging every action and requiring human approval for sensitive steps. No system is perfect, so we design for detection and recovery as well as prevention.
We choose per task based on quality, latency, cost and data requirements, and design the system so models can be swapped as better options appear.
Only where you decide the risk is acceptable. Low-risk actions can run automatically; anything affecting money, customers or data can require explicit approval.
Learn more
Five illustrative n8n workflows for small businesses, from lead triage to weekly reporting, with architecture, implementation steps, security limits and licensing notes.
Learn how AI agents differ from chatbots, when tool use helps, how to test an agent, and which permissions and safety checks to apply before using one.
Learn Claude Code setup, model choices, context management, subscription versus API billing, token costs, and practical ways to reduce usage safely.
Book a consultation and we'll help you work out scope, approach and a sensible first step.
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