Whole workflows, not single steps.
iLeaf builds agents that carry a business process from start to finish: reading the request, calling the systems involved, making the decision and recording why. Unlike scripted automation, they handle the cases nobody enumerated — and unlike unattended AI, they escalate to a human rather than guessing when confidence is low.
Ask Lia about agentic automationWhat this includes
- Process discovery
- We trace how the work is genuinely done today, including the exceptions handled informally over chat and email.
- Multi-agent workflows
- Specialised agents for each part of a process, coordinated by a supervisor that can halt the whole run.
- Human-in-the-loop
- Approval gates on the steps that carry consequence, so automation earns trust rather than assuming it.
- System integration
- CRM, ERP, ticketing, email and telephony wired in as tools with explicit permissions per system.
- Exception handling
- Anything outside the agent's competence is routed to a person with the context already assembled.
- Outcome measurement
- Completion rate, escalation rate, handling time and cost per case, tracked from the first week.
How we run it
Every phase ships something usable on its own, so you are never holding a half-finished system waiting on the next milestone.
Pick a process with a real cost
We look for volume, repetition and a measurable cost of delay. Automating something rare is rarely worth the engineering.
Automate the middle first
The predictable majority of cases get automated; the hard tail stays with people until the data says otherwise.
Run alongside the humans
The agent shadows the existing process first, so its decisions can be compared against real ones before it takes over.
Widen on evidence
Scope grows only where completion and escalation rates justify it, and the approval gates come off last.
What we build it with
- LangGraph
- Model Context Protocol (MCP)
- Temporal
- Python
- TypeScript
- Claude
- PostgreSQL
- Redis
- Twilio
- Webhooks
- OpenTelemetry
- Governor Engine
Questions we get asked
How is this different from RPA?
RPA replays fixed steps and breaks when a screen or field changes. An agent works from the goal, chooses which tools to call, and copes with cases nobody scripted. The trade-off is that it needs governance — limits, approvals and audit — which is the engineering RPA does not require and agents cannot go without.
Will it replace our team?
In the work we have delivered it absorbs the repetitive majority and routes the difficult cases to people with the context already gathered. The honest framing is capacity, not headcount: the same team handles materially more volume, and spends its time on the exceptions that need judgement.
How do we know it is making good decisions?
Every run records the goal, the tools called, the reasoning and the outcome, so any decision can be reconstructed afterwards. Before going live the agent shadows the existing process, and its decisions are compared against your team's on the same cases.
Let’s talk about agentic automation.
Tell us what you are running and what it needs to do next. We will tell you honestly whether we are the right team for it.