Connect your systems. Automate the work between them.
ETT designs workflows that combine AI, rules-based automation and human decisions. Reduce repeated handling, move information between systems and give teams a clear way to manage exceptions.
What you would be buying
A working automation for one agreed workflow, connected to your systems, with the exception handling and documentation needed to run it.
What you receive
- Process design covering the current and the target workflow
- Integration and permission map: which systems, which accounts, which actions are allowed
- Working automation deployed to the agreed environment
- Exception handling, including what happens when data is missing or permission is denied
- Test evidence against the acceptance criteria agreed at the start
- Operating documentation and support handover
What you provide
- Access to the process and the people who run it today
- Representative test data
- Integration credentials, issued through your own secure process
- Named system owners who can approve connections
- Acceptance criteria agreed before build starts
How success is measured
- End-to-end cycle time against the pre-agreed baseline
- Successful completion rate
- Exception rate, and how many exceptions a person had to resolve
- Rework: how often a completed item had to be corrected
- Cost per completed transaction
Not every workflow needs an agent. Where a process is predictable and the rules are stable, deterministic automation is cheaper to build, cheaper to run and easier to assure. We use AI interpretation where the input varies or the next step depends on meaning, and say which is which in the design.
What can hold it up
- Integration credentials arriving on time — usually the single largest cause of delay
- A stable target process; a workflow being redesigned in parallel cannot be automated reliably
- Somebody with the authority to sign off what the automation may do without a human
After it goes live
Automations need an owner after go-live. We hand over runbooks and monitoring, and can provide ongoing support under a separate agreement — a commercial arrangement, not something included by default.
Deliverables and measures describe the standard shape of this engagement. Exact scope, duration and commercial terms are agreed and confirmed in writing before work starts.
How we do the work
AI agent and automation design
We define where AI agents can create operational value, what they need to connect to, and how they should behave inside the wider workflow.
Workflow and process automation
We design automation around real business processes, reducing manual handovers, repetitive tasks and avoidable friction across teams and systems.
RPA, AI and LLM integration
We bring together existing automation platforms, AI models, LLM capabilities and enterprise systems so they work as part of a connected architecture.
Agentic architecture blueprint
We map the systems, data, permissions, triggers, escalation points and governance needed to move from an idea or pilot into live operation.
Use case prioritization
We identify which automation opportunities are viable, valuable and worth building first, based on business impact, complexity, data readiness and risk.
Live optimization
We support automation after deployment, using performance data and operational feedback to improve workflows and keep systems aligned to business goals.
Where agentic automation can create value
Customer service workflows
AI agents can interpret customer requests, retrieve relevant information, support triage and trigger the next action across CRM, ticketing or contact center platforms.
Why this matters
Most enterprises already have automation somewhere in the business. Some have RPA platforms, chatbots, workflow tools or AI assistants. The issue is that these tools often sit in separate places and solve isolated problems.
A chatbot may answer a question but not trigger the next workflow. An automation may complete a task but not understand the context around it. A team may test an AI agent without connecting it to the systems, data and controls needed for live operation.
ETT closes that gap by designing agentic automation around the way the organization actually works. We connect AI agents, workflows, enterprise systems and governance into a controlled operating layer, so automation can move from simple task completion to meaningful business action.
What agentic automation means
Agentic automation combines AI agents with workflow design, data access, system integration and governance.
Traditional automation usually follows fixed rules. It works well when a process is predictable, but it can struggle when the work requires context, interpretation or flexible decision-making.
Agentic AI adds a more intelligent layer. An AI agent can interpret information, understand intent, decide what should happen next within defined boundaries, and trigger actions across connected systems.
For enterprises, the value is not the agent on its own. The value comes from what the agent is connected to, what it is allowed to do, and how safely it can operate inside live business processes.
Rule-based, task-focused, fixed workflows.
Context-aware, workflow-connected, governed action.
How we build agentic automation into the flow of work
Orient
We identify where automation can create measurable operational value, looking at process friction, manual handovers, data availability, system complexity and risk.
Prove
We prove the agentic workflow on a real, risk-bearing process, with full traceability and measured outcomes before anything is scaled.
Govern
We define and enforce the guardrails — permissions, escalation points, governance controls and the human accountability the agents must respect.
Scale
We implement and orchestrate AI-driven workflows into live environments, connecting agents to the systems and processes they support across the business.
Compound
We monitor performance, optimize workflows and extend the pattern to adjacent processes as the business learns from live usage.
Often delivered alongside
Timing, cost, access and ownership
How long does this take?
A first workflow is a matter of weeks rather than months, but the schedule is set by integration access more than by build effort. Credentials arriving late is the most common cause of delay in this work, and we sequence around getting them early.
What drives the cost?
The number of systems to integrate and the state of their interfaces; how much variation the process contains; and how much of the work genuinely needs AI interpretation rather than deterministic rules. A predictable process behind good APIs is considerably cheaper than a variable one behind a legacy screen.
What access do you need?
Access to the process and the people who run it, representative test data, and integration credentials issued through your own secure process. We do not ask for shared accounts, and credentials are scoped to the actions the workflow needs.
Who owns what you build?
Ownership of the solution, its configuration and the documentation is set out in the engagement contract before work starts. Where a third-party platform forms part of the solution, that platform's own licensing terms apply to it, and we identify which components those are rather than leaving the boundary vague.
What happens when it goes wrong?
The workflow stops and escalates. Every automation we build has a defined failure path: what happens when a source is unavailable, a validation fails or permission is denied, and who it goes to. That path is designed at the start, not added after the first incident.
About this service
What is agentic automation?
Agentic automation combines AI agents with workflow design, system integration and governance. It allows AI to support decisions and trigger actions within defined business processes.
How is agentic automation different from traditional automation?
Traditional automation usually follows fixed rules. Agentic automation can work with more context, interpret information and support more flexible workflows, while still operating within controlled boundaries.
What can AI agents do in a business?
They can triage requests, retrieve information, update systems, trigger workflows, summarize cases, support internal teams and escalate complex issues when human input is needed.
Does agentic automation replace existing systems?
No. In most cases it works with existing systems by connecting data, workflows and actions across the tools a business already uses.
What does agentic automation need to work properly?
Clear use cases, reliable data, system integrations, governance controls, permissions, escalation routes and ongoing performance monitoring.
Ready to turn automation into operational capability?
Request an AI session to explore where agentic AI and automation could reduce friction, connect workflows and create measurable value inside your organization.
Around four minutes. Indicative guidance based on your answers.