Intelligent Workflow Automation
Intelligent workflow automation replaces manual operational steps with durable, event-driven software that retries safely, records every state transition, and escalates only genuine exceptions to a human. Pruning Labs maps the real process, automates the deterministic parts, applies AI only where judgement is required, and gives operators a console for the rest.
Automate the process you actually run, not the one on the slide
Every automation project starts with a process map that turns out to be wrong. The documented flow has six steps; the real flow has six steps plus four exceptions handled over WhatsApp by one person who has been there five years.
We spend the first week watching the real work. What we automate afterwards matches reality - including the exceptions, which is where most automation projects quietly break.
Idempotency is not optional
Automation that cannot safely run twice is a liability. Every workflow we build is keyed, so a retry after a partial failure re-enters the same state rather than double-charging a customer or double-shipping an order.
We use durable execution - Temporal or an equivalent state machine on queues - so a workflow interrupted by a deploy, a network partition, or a provider outage resumes exactly where it stopped, with full history.
- Idempotency keys on every side-effecting step
- Durable workflow state, replayable end to end
- Backpressure and rate limiting on every third-party integration
- Dead-letter handling with a human-facing queue, not a silent log line
Where AI belongs in an automation
Classification, extraction, summarisation, and drafting. That is where a model earns its place - the judgement steps that used to require a human read.
Everything else stays deterministic. Routing rules, calculations, thresholds, and state transitions are ordinary code, because they must be correct every single time and explainable when audited.
The operator console is the product
Automation does not remove humans; it changes what they look at. Your team stops doing the ninety-five percent that is mechanical and starts working the five percent that is genuinely ambiguous.
So we build for that: a queue of exceptions with the full context attached, one-click overrides, an audit trail of every automated decision, and alerting that fires on stalled work rather than on every event.
What you get
- Process map of the real workflow, exceptions included
- Durable, idempotent workflow implementation
- Integrations with your existing systems of record
- Exception queue and operator console
- Alerting on stalled or failed work with runbooks
- Throughput and cycle-time telemetry from day one
Typical stack
- Temporal
- Go
- Python
- PostgreSQL
- Cloudflare Workers & Queues
- Stripe
- Slack / WhatsApp Business API
Related work
Medikle
How Pruning Labs built a deterministic dosage engine and multilingual patient assistant for Medikle, cutting intake from a five-step form to a single conversation.
Heirview
How Pruning Labs replaced blunt dunning emails and fragile cron retries with a decline-code-aware retry state machine on Go and Temporal for Heirview.
Further reading
When not to build an AI agent
Multi-agent architectures are oversold. A practical test for whether a workload needs an agent, a single model with tools, or ordinary deterministic code.
Idempotency is the whole job
Automation that cannot safely run twice is a liability. Idempotency keys, durable execution, and decline-code-aware retries - the patterns that keep automated money movement correct.
Questions we get asked
Is this the same as a no-code automation tool?
No. No-code tools are excellent for low-volume, low-consequence glue. We are engaged when volume, correctness, or auditability makes that fragile - when a silent failure costs money, or when a flow needs versioning, tests, and replay.
Will automation replace our operations team?
In our engagements it changes what they do rather than removing them. Mechanical throughput moves to software; the team moves onto exceptions, quality, and the work that needed judgement all along.
How do you handle integrations with legacy systems that have no API?
Depending on what exists we work with database replicas, SFTP drops, scheduled exports, or a thin adapter service. We avoid brittle screen-scraping unless there is genuinely no alternative, and when there is not, we isolate it behind a contract so the rest of the system stays clean.