Tasks too messy for simple rules
Some jobs involve reading emails, PDFs or notes that never look the same twice, so standard automation breaks.
AI AGENTS
AI Agents That Handle Defined Tasks Inside Your Systems
We build AI agents that can read information, decide on next steps within set limits and take actions in your tools, with a person stepping in when needed.
AI Agent Development
OVERVIEW
What AI Agent Development Involves
An AI agent is a step beyond a chatbot. Instead of only answering questions, it can follow a goal through several steps: look something up, check a record, write a draft, update a system and report back. Think of it as a junior assistant that works fast but needs clear instructions and supervision.
We design agents around one job at a time. That might be researching inbound leads before a sales call, sorting supplier invoices, or preparing weekly summaries from several data sources. Narrow agents are easier to test and far more dependable than a general helper that tries to do everything.
We're upfront about what agents can't do well. They can misunderstand ambiguous requests and they don't carry real accountability, so we give them limited permissions and add approval steps wherever an error would be costly.
COMMON PROBLEMS
Problems This Helps Solve
Senior staff doing junior work
Experienced people spend hours gathering information before they can make a decision.
Knowledge buried in documents
Policies, product specs and past answers sit in folders nobody has time to read through.
Multi step jobs that stall
A task needs input from three systems, so it waits in someone's queue for days.
WHAT'S INCLUDED
What You Get
Task and scope definition
A written description of the agent's job, what it can access, what it may change and when it must stop.
Tool and data connections
Secure connections to the apps and data the agent needs, using the least access possible.
Prompt and instruction design
Carefully written instructions and examples so the agent behaves consistently.
Knowledge retrieval setup
Where needed, a searchable index of your documents so the agent answers from your material, not guesswork.
Approval and review steps
Checkpoints where a person confirms actions before anything is sent or changed.
Logging and evaluation
Records of each run and a test set of examples so we can check quality over time.
HOW IT WORKS
How It Works in Practice
The agent is started by an event (a new email, a form entry, a button in your CRM) or on a schedule. It receives a goal and a list of tools it's allowed to use, such as searching a knowledge base, reading a spreadsheet or creating a draft. It works through the steps, and every action it takes is logged.
For anything that changes customer records, sends messages externally or spends money, the agent prepares the action and waits. A team member reviews it in Slack, email or a simple dashboard and approves, edits or rejects it.
Benefits
- Less time gathering information
The agent pulls together what your team needs so they can go straight to the decision.
- Consistent first drafts
Replies, summaries and reports start from the same structure every time.
- Controlled risk
Limited permissions and approval steps keep mistakes from reaching customers.
- Room to grow
Once one agent works well, the same foundation can support new tasks.
OUR PROCESS
How We Approach AI Agent Development
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01
Shadow the current task
We watch how a person does the job today and collect real examples, including the awkward ones.
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02
Define permissions and stop points
We agree which tools the agent can use, what it can read versus change, and where it must hand off.
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03
Build a working prototype
We build the agent against a small set of examples and review its output with you.
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04
Evaluate and harden
We test against a larger example set, tighten instructions and add guardrails for failure cases.
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05
Supervised rollout
The agent runs with full human review at first, and we loosen that only where results are reliable.
Tools and technology
Chosen to fit the project. We'll use your existing tools where they do the job.
- OpenAI APIs
- Claude API
- LangChain
- n8n
- Vector databases
- Google Workspace
- Slack
- REST APIs
- Webhooks
Where it can help
- Preparing prospect research before sales calls
- Sorting and extracting data from invoices or forms
- Answering staff questions from internal policies
- Compiling weekly reports from several tools
- Triaging a shared inbox into categories and drafts
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Learn moreFAQS
Questions About AI Agent Development
How is an AI agent different from a chatbot?
A chatbot mainly holds a conversation. An agent works toward a goal and can use tools, like searching documents or updating a record, across several steps.
Can an agent run without anyone checking it?
For low risk tasks like tagging or summarising, often yes after testing. For anything external facing or financial, we keep a human approval step. Agents shouldn't be trusted alone with decisions that carry real consequences.
Is our data used to train AI models?
We use business API settings that, according to the providers' published terms, don't use your inputs for training by default. We also limit what data the agent can see in the first place.
What if the agent gets something wrong?
Every run is logged, so we can see what it did and why. We use those cases to adjust instructions and add checks.
How long does a first agent take to build?
It depends on how many systems are involved and how clear the task is. We'll give you an estimate after the scoping step, not before.
GET STARTED
Have a task in mind for an agent?
Describe the job and share a few real examples. We'll tell you honestly whether an agent is the right fit.