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AGTai agent development

AI agent development with limits you can see.

Founcy Solutions' AI agent development builds agents that do more than chat: they read a request, look things up in your systems and take the next step, from updating your CRM to booking a callback. Every agent gets clear permissions, an approval gate for anything risky and a log of everything it did.

Scope my AI agent → What's included
  • approval gates on risky actions
  • every action logged
  • you own the setup
agent.toolsillustration
  • a request reaches the agent, which checks your policy notes
  • it reads the CRM with read-only access
  • risky actions wait for a manager's approval
  • every step is written to an audit log
scoped permissionsapproval gatesaudit log
definition

What is AI agent development?

AI agent development is the design and build of software agents that use a language model to understand a request, decide which steps to take and then use your tools to carry them out: searching documents, reading or updating your CRM, booking appointments or drafting replies. Unlike a basic chatbot, an agent can act, so the work is as much about permissions, approval steps and logging as it is about the AI model. Done well, it takes repetitive multi-step tasks off your team while people stay in charge of the decisions that matter.

Best for
Repetitive tasks that follow rules but need reading and judgement
Works with
Your CRM, inbox, calendar, documents and APIs
Control
Scoped permissions, approval gates and an audit log
Ownership
You own the accounts, prompts and workflows we build
sound familiar?

Signs an AI agent could take the load.

Tap what applies. We'll use your answers to pick the first task worth handing to an agent.

Tap anything that sounds familiar.

what's included

AI agent development, end to end.

From the first task map to monitoring after launch. The scope depends on how many tasks and systems the agent touches.

01

Task mapping

We break the job into steps, decide which ones the agent handles and where a person must approve, before any build work starts.

02

Custom agent build

Prompts, tools and decision rules built for your task, on the model that suits the job, the data and the running cost.

03

CRM and API integrations

Connections to your CRM, inbox, calendar, documents or your own APIs, each with the narrowest access the task needs.

04

Support and sales agents

Customer-facing agents that answer from your approved information, qualify enquiries and hand over cleanly to your team.

05

Internal productivity agents

Back-office agents for admin, summaries, data entry and follow-ups, set up where your team already works.

06

Monitoring and improvement

Logs, failure alerts and regular reviews of real runs, so the agent gets better and stays within its limits.

built to be trusted

An AI demo, or an agent you can trust?

Agents are easy to demo and harder to run safely. The difference is in the guardrails around the model.

oversight: an afterthought
PermissionsBroad access to make the demo workLeast-privilege access per tool, reviewed before launch
Risky actionsThe agent acts on its ownRefunds, deletions and payments wait for a person's approval
AnswersWhatever the model remembersAnswers grounded in your approved documents
FailuresSilent errors nobody noticesA fallback to a person and an alert when something fails
RecordsNo trace of what it didEvery step written to an audit log you can review
ResultsIt feels fasterTime and accuracy measured against a baseline from before launch
how it works

From one task to a working agent.

Founcy's three-step approach, with a pilot before anything scales.

  1. 01

    Free discovery call

    A 20-minute call to understand the task, the systems involved and what a good result looks like.

    → a shortlist of agent tasks
  2. 02

    Map and plan

    We map each step, the data it needs, the actions allowed and where a person approves. You sign off the plan.

    → agent blueprint
  3. 03

    Build and integrate

    We build the agent, connect it to your tools with scoped access and test it on real examples.

    → tested agent on staging
  4. 04

    Pilot launch

    The agent goes live on one task with close monitoring, and we compare the results with your baseline.

    → pilot results
  5. 05

    Optimise and extend

    We tune prompts and rules from real runs, then add the next task once the first one has proved itself.

    → next task, if it's worth it
stack

Tools picked for each agent.

We choose models and services based on the data involved, the accuracy needed and the running cost.

// models
OpenAIAnthropicGoogle GeminiOpen-source models
// orchestration
n8nMakeZapierCustom Python
// knowledge
Vector databasesGoogle DriveNotionSharePoint
// systems
HubSpotGoHighLevelSalesforceGoogle WorkspaceMicrosoft 365
what drives the cost

What does an AI agent cost to build?

Build cost and running cost are separate. These four drivers shape the build; model and tool usage is estimated on its own.

Tasks

Each task needs its own steps, rules and tests.

Systems connected

Every connection needs access set-up, error handling and testing.

Actions allowed

Agents that change records or send messages need approval steps and more testing.

Who uses it

Customer-facing agents need stricter wording, tone and handover rules.

keep exploring

Related services.

BOT

AI chatbot development

Website chat that answers from your approved information.

Explore → VOX

AI voice agents

Phone calls answered, booked and handed over to your team.

Explore → BPA

AI business process automation

Whole processes automated, from documents to approvals.

Explore → AIA

AI automation services

Where AI automation pays, and what we won't automate.

Explore →
faq

AI agent questions.

A chatbot answers questions. An AI agent can also take steps for you, such as checking an order in your CRM, booking an appointment or drafting a follow-up. Because agents can act, we give each one scoped permissions, approval steps for risky actions and a full log.
No. Agents work best on repetitive, rule-based steps such as lookups, data entry, first drafts and routing. People keep the judgement calls, the exceptions and the relationships. The aim is to give your team time back.
We limit what it can access, ground its answers in your approved information, require a person's approval for anything risky and test it on real examples before launch. After launch we review the logs and fix what we find. No AI is perfect, so we design it to fail safely.
Before building, we map where your data goes: which systems the agent reads, which model provider processes it and what is stored. We choose providers and settings to suit how sensitive the data is, and the accounts are yours.
It depends on the number of tasks, the systems connected, the actions the agent may take and who uses it. Use the cost builder above to see where you sit. After a free 20-minute call you get a quote within 24 hours, with running costs estimated separately.
A pilot agent on one task is the quickest place to start. Timelines depend on the systems involved and how quickly access is set up, and you get a dated plan after discovery, before any build work starts.
start with one task

Which task should your first agent take?

Send your website and tell us which job eats your team's time. We'll come back with the agent worth piloting first and the guardrails it needs.

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free 20-minute call · quote within 24 hours · a person approves the risky steps