AI agent development for business
Custom AI agents that do real work, with a person in the loop
Tenaxity designs, builds and monitors custom AI agents that sort your inbox, update your CRM, research prospects and handle back-office tasks. Every AI agent for business we build has clear permissions, an approval step for anything that matters and a log of every action.
What an AI agent is, and how it differs from a fixed automation
A fixed automation follows the same steps every time: when a form arrives, create a contact and send an email. An AI agent is given a goal, a set of tools and some rules, and it decides which steps to take based on what it finds. That flexibility is useful for messy work, and it is also why agents need tighter controls than ordinary workflows.
Fixed automation
Best when the input is predictable and the steps never change. It is fast, cheap to run and easy to test. Most workflows we build are still this kind.
AI agent
Best when the input is unstructured, such as an email, a document or a web page, and the next step depends on what it says. The agent reads, chooses a tool, checks the result, then carries on or asks a person for help.
Agentic operations
Several agents and fixed workflows working together across a whole process, such as enquiry to invoice. An orchestrator agent hands work to the others and escalates exceptions to a person. This is what people usually mean by agentic business operations.
Types of AI agents we build
A good AI agent for business does one job well before it is given a second. These are the patterns we see most often, each described by what it reads, what it can do and where a person signs off.
Inbox and triage agent
Reads incoming email, sorts it by intent and urgency, pulls the key details into your CRM or helpdesk and drafts replies for a person to approve. It pairs well with rules-based AI email automation for the simpler parts of the inbox.
Research and outreach agent
Given a list of target accounts, it researches each company from its website and public sources, writes a short brief and drafts a first message suited to the contact's role. A person reviews every message before it is sent.
Operations and back-office agent
Handles the admin between systems: checking orders against invoices, chasing missing documents, updating spreadsheets and raising an exception when the numbers do not match.
Support agent
Answers common customer questions from your help content, looks up order or account status and hands the conversation to a person when the topic is sensitive or the agent is unsure. See our AI customer support automation page for the wider workflow.
Reporting agent
Pulls figures from your CRM, finance and marketing tools on a schedule, checks for gaps and writes a short plain-English summary of what changed and what needs attention this week.
Orchestrator agent
Coordinates the others. It receives a task, decides which agent or workflow should handle each part, tracks progress and escalates anything that gets stuck. It earns its place once two or three agents are running.
How we build an AI agent, step by step
Our AI agent development services follow the same path on every project. The aim is to get a useful agent into real use quickly, without letting it loose on your systems before it has earned trust.
Scope
We agree one job, its inputs, what counts as done and what the agent must never do.
Map tools
We list the systems the agent needs, such as your inbox, CRM or shared drive, and the exact actions it may take in each.
Set permissions
The agent gets its own access with the least rights needed, read-only wherever possible.
Add knowledge
Policies, price lists, templates and past examples are prepared so the agent works from your information, not guesswork.
Test
We run the agent on a set of real past examples and compare its output with what your team actually did.
Roll out with approval
At first the agent drafts and a person approves. Actions become automatic only when you are happy with the results.
Monitor
Runs, errors, costs and approval rates are logged so problems show up early.
Improve
We review failures and edge cases and adjust prompts, rules and tools as the work changes.
Safety and monitoring are part of the build
When you compare any AI agent development company, ask how it controls what the agent can do. An agent that acts on your systems needs the same controls you would give a new member of staff, plus a few more, because it works fast and never gets tired.
Controls in every agent
- Least-privilege permissions for each tool
- Approval steps before sending, paying, deleting or changing key records
- A log of every input, decision and action
- Limits on volume, spend and the number of steps per task
- Fallbacks that hand work to a person when the agent is unsure or a tool fails
What we monitor
- Failed or stuck runs
- Approval and rejection rates
- Model usage and cost per task
- Unusual activity, such as a sudden spike in actions
- Feedback from the people reviewing the agent's work
When an agent is the wrong choice
Agents are not always the answer. During the audit we check whether a simpler build would do the job better.
The steps never change
If the input is structured and the rules are fixed, a normal workflow is cheaper, faster and easier to test than an agent.
A mistake would be costly
Legal advice, payments and decisions about people should stay with a person. An agent can prepare the information, but it should not make the call.
There is nothing to work from
If the process is not written down and there are no past examples, we document the process first. Otherwise the agent has no standard to meet.
A worked example
This is a hypothetical business, used to show the arithmetic rather than a client result. Your own numbers will differ.
Before
For example, say a 12-person services business gets 80 emails a day in a shared inbox, and someone spends 2 minutes on each one reading, sorting and logging it in the CRM. That is 160 minutes a day, or 800 minutes (about 13 hours 20 minutes) over a five-day week.
After
An inbox agent sorts each email, logs it in the CRM and drafts a reply. A person spends about 30 seconds per email checking its work: 40 minutes a day, or 200 minutes (3 hours 20 minutes) a week.
What changes
Under those assumptions the team saves 600 minutes, or 10 hours, a week. Every email still passes a person, and the CRM is updated the same day.
Tools we build agents with
We usually build on n8n for the workflow and tool layer, with language models from OpenAI or Anthropic doing the reading and reasoning. We can connect to the systems you already use.
Models and orchestration
n8n for workflows, tools, schedules and logging. Models from OpenAI and Anthropic, chosen per task for quality, speed and cost.
Email and chat
Gmail and Outlook for inbox agents. Slack and Microsoft Teams for approvals, alerts and the agent asking your team a question.
Business systems
Your CRM, such as HubSpot, Pipedrive or Salesforce. Google Workspace and Microsoft 365 for documents, sheets and calendars, plus other tools with an API.
Costs and where to start
Larger agent builds are scoped and quoted as a custom project after the free audit. Ongoing builds and orchestrator agents can also run inside our monthly packages, which are listed on the pricing page. If you are not yet sure whether you need an agent, a roadmap or a simpler workflow, our AI consulting work starts with that question.
FAQs
Short answers for teams deciding whether an AI agent is worth building.
What is an AI agent for business?
An AI agent for business is software that uses a language model to work towards a goal with a set of tools, such as reading an inbox, looking up a CRM record or drafting a reply. Unlike a fixed automation, it decides its next step from what it finds, within the permissions and rules you set.
How is an AI agent different from a chatbot?
A chatbot answers questions in a conversation. An agent can also take actions in other systems, such as updating a record or creating a task, and it can work in the background without anyone chatting to it.
Will the agent act without anyone checking?
Not at first. A new agent drafts and a person approves. Once the results are consistent, you decide which low-risk actions can run automatically. Anything that sends, pays, deletes or changes important records can stay behind an approval step for good.
What happens when the agent gets something wrong?
Every action is logged, so we can see what the agent read, what it decided and why. We review mistakes and change the prompt, rules, knowledge or permissions so the same case is handled properly next time. Uncertain cases go to a person instead of being guessed.
Which AI models do you use?
We work with models from OpenAI and Anthropic and choose per task, balancing quality, speed and cost. The workflow layer is usually n8n, which means the model can be changed later without rebuilding the agent.
Is our data safe with an AI agent?
Each agent gets the minimum access it needs, credentials are kept in the workflow platform rather than in prompts, and everything the agent touches is logged. Where data residency matters, n8n can be self-hosted. We agree how your data is handled during scoping.
How long does it take to build an AI agent?
It depends on the job and how many systems are involved. We give you a scope and a timeline after the free audit, once we have seen the workflow and the tools it needs to use.
How do we get started?
Book a free AI workflow audit. We look at the work you want to hand off, check whether an agent or a simpler workflow fits better, and outline the first build.
Start with one agent on one job
Book a free audit and we will look at the work you want to hand off, decide whether an agent or a fixed workflow fits, and agree the approval points before anything is built.