Document intake
Turn incoming documents into validated records.
- 01Receive document
- 02Extract fields
- 03Validate against rules
- 04File record
- 05Queue incomplete items
Solution · Agents that complete tasks
A chatbot talks. An agent gets work done.
AI agents observe, reason within defined boundaries, use tools, take actions, verify the result, and escalate when needed. We build them for workflows where that loop saves real time.
Intake Agent
Document processing
- Task
- Validating fields extracted from a supplier invoice
- Tools
- InboxDocument AIERP
- Approval
- Required above the agreed amount threshold
- Last action
- Matched invoice to purchase order
AutonomySupervised
What it is
An agent is defined by what it can do, safely
A language model on its own produces text. An agent wraps that model in a loop: it reads the current situation, decides what to do next, calls a tool, checks what happened, and repeats until the task is done or it needs help.
The engineering challenge is not making an agent capable — it is making it predictable. That means narrow permissions, explicit rules, measured behaviour, and a clear point where a person takes over.
Capabilities
What it can do
Observe
Read only the systems, data, and context it has been authorised to access.
Plan
Decide the next step against explicit rules, policies, and instructions.
Act
Call approved tools, update records, or prepare drafts.
Verify
Check results, record what happened, and catch its own errors where possible.
Escalate
Hand uncertain or out-of-policy work to a person.
Report
Produce a readable trace of every run.
The operating loop
From request to action — with control built in.
- 01
Observe
The agent reads only the systems, data, and context it has been authorised to access.
- 02
Plan
It determines the next step against explicit business rules, policies, and instructions.
- 03
Act
It calls approved tools, updates records, drafts communication, or performs an allowed task.
- 04
Verify
It checks the result, records what happened, and escalates anything uncertain or outside policy.
Chatbot vs agent
What actually changes
| Dimension | Chatbot | AI agent |
|---|---|---|
| Primary output | Answers and generated text | Completed tasks and updated systems |
| Who drives the work | Waits for the user to ask | Works through a defined task |
| Context | Mostly the current conversation | Authorised systems, records, and policy |
| Tools | Usually none | Approved tools with scoped permissions |
| Effect on your systems | Does not necessarily change anything | Updates records within allowed limits |
| Checking its work | Rarely | Verifies results and logs every step |
| When unsure | Answers anyway | Escalates to a person |
In practice
Common patterns
Request triage
Classify and route requests with context attached.
- 01Read request
- 02Gather context
- 03Classify
- 04Route to owner
- 05Draft first reply
Reconciliation
Compare and resolve differences between systems.
- 01Pull both datasets
- 02Match records
- 03Resolve clear cases
- 04Queue the rest
- 05Summarise
Human in the loop
Control when it matters.
Autonomy is earned task by task. These controls are part of every deployment.
- Scoped credentials and tool allow-lists
- Approval thresholds for consequential actions
- Audit logs of observations, decisions, and actions
- Evaluation suites before every release
- Kill switch and rollback
Honest fit
Where it works — and where it doesn't yet
Good fit
- Multi-step tasks that span several tools
- Inputs that vary too much for simple rules
- Work with clear criteria for 'done'
- Processes where errors can be caught and corrected
Not the right starting point
- Tasks with no clear success criteria
- Irreversible actions with no review step
- Processes that change daily
- Workflows with very low volume
How we deliver it
Services behind this solution
Service
Agentic AI Development
We design and build agents that observe, plan, act, and verify within explicit boundaries — connected to your tools, logged end to end, and supervised until they have earned more autonomy.
Service
AI Integration
We integrate AI capabilities into your CRM, ERP, help desk, databases, and internal applications — through secure APIs, webhooks, and middleware — so intelligence is available where decisions are made.
Service
Ongoing AI Engineering
AI systems need care after launch — models change, data drifts, processes evolve. We provide continuous engineering to monitor, improve, and extend your AI and software over time.
FAQ
Frequently asked questions
01What is the difference between an AI agent and an AI employee?
An agent is the technical pattern — a system that uses tools in a loop to complete tasks. An AI employee is one or more agents packaged around a business role, with knowledge, supervision, and reporting.
02Are AI agents safe to connect to our systems?
They can be, when access is scoped, actions are logged, consequential steps require approval, and behaviour is tested before release. Safety comes from engineering, not from the model alone.
03Can agents work together?
Yes, multi-agent workflows are possible. We only use them when they make a system simpler to reason about — not by default.
Related solutions
Explore further
Role-based digital workers
AI Employees
An AI employee is a supervised digital worker designed around a specific business function. It understands context, uses approved tools, completes tasks, and escalates decisions that need human judgment.
Repetitive work, automated
AI Automation
AI automation combines reliable workflow logic with AI that can read documents, understand messages, and classify information. The result: routine work moves on its own, and people review only what needs them.
End-to-end processes
Business Process Automation
Business process automation connects the steps of a workflow — intake, validation, approval, update, notification — across teams and systems, so work moves forward without someone pushing it along.
Next step
Bring us the process your team is tired of doing manually.
A strategy call is a working conversation about one workflow: what it involves today, what could be automated, and what a first build would look like.
What happens next
- 1Tell us about one workflow
- 2We review it before replying
- 3A working session, then a first-build proposal