Agents That Run the Process, Humans Keep the Keys
AI agents that execute multi-step workflows across your systems: every step logged, every irreversible action checkpointed by a human.
Rule-based automation breaks on variation; copy-paste work eats your team alive. Our agents read unstructured inputs, decide between paths, and act through governed APIs with scoped credentials. The governance, checkpoints, and observability already run our own products and our banking agent at ~500K conversations a month. That is why our builds move fast: the hard parts already exist.
- Invoice parsed · vendor matched DONE
- PO cross-checked · amounts agree DONE
- GL code proposed · 6420-OPS DONE
- Payment of $18,400 scheduled AWAITING APPROVAL
- ERP entry + vendor confirmation QUEUED
Why Automation Projects Stall
The projects that stall rarely fail on model quality. They fail on process selection, undocumented workflows, and missing governance. These six patterns recur across automation efforts we get called in to fix.
The Undocumented Process
The workflow lives in someone's head, fragmented across six tools and a dozen judgment calls nobody wrote down. You cannot automate a process nobody can describe.
Automated the Wrong Thing
The process was broken before the AI arrived, or picked for optics instead of volume and risk. No baseline metric means no way to prove value, so sponsorship fades by the second demo.
Happy-Path Automation
The workflow works until an invoice arrives in a new format, a vendor is missing, or a field is empty. Exceptions pile up and the team does the work anyway.
Pilot Purgatory
The proof of concept works. Then nothing: no named owner, no agreed success metric, no path from demo to production. Nearly half of AI projects now die between PoC and adoption.
Agents With Blank Checks
Automation deployed with broad credentials and no checkpoints. One bad action in production and the whole program gets shut down.
No Audit Trail
Actions happen inside a black box. When finance or compliance asks what the system did and why, the answer is a shrug and a log file nobody can read.
Workflows We Automate
Six families of multi-step work, each deployed with checkpoints and an audit trail. Most programs start with one workflow, prove the numbers, then expand.
Document Processing
Invoices, contracts, claims, and forms: extract, validate, cross-check, and route for approval without a human retyping anything.
Support Operations
Ticket triage, enrichment, routing, and resolution drafting, with escalation to agents when confidence drops below the bar.
Finance & Back Office
Reconciliation, expense validation, payment scheduling, and reporting cycles that run unattended until a checkpoint needs eyes.
Sales & CRM Operations
Lead qualification, enrichment, follow-ups, and pipeline hygiene: the CRM stays current because an agent keeps it that way.
Compliance & Monitoring
Continuous checks on vendors, contracts, and certifications: watch, flag, and escalate instead of auditing once a quarter.
Cross-System Orchestration
Work that spans tools: read from the ERP, decide, write to the CRM, notify in Slack, all under one governed agent run.
Why Our Automations Ship Faster
Most builds assemble governance, checkpoints, and observability from scratch. Ours start from the infrastructure that already runs our products and our banking agent in production. That is where the speed comes from.
What every run clears
- Input validation: malformed payloads and injection attempts rejected before the agent actsEVERY EVENT
- Scoped credentials: agents touch only what the workflow needsEVERY ACTION
- Human checkpoints: irreversible steps wait for approvalEVERY RUN
- Confidence routing: low-confidence cases go to a human, with contextEVERY CASE
- Eval gates: accuracy measured on real cases before expansionEVERY RELEASE
- Feedback loop: exceptions and overrides feed the next eval setCONTINUOUS
- Governance layer: policy enforcement, audit log, kill switchALWAYS ON
- Operations: kill-switch drills, rollback runbooks, regression checks on model updatesALWAYS ON
- Observability: every run traced, every action logged with evidenceCONTINUOUS
What You Own at Launch
A running workflow and everything needed to operate, extend, and audit it, in your accounts from day one.
The launch handover
- Workflow live in production, wired to your systems
- Process map with every decision point documented
- Input validation layer: malformed payloads and injection screened before the agent acts
- Checkpoint design: which steps need human sign-off
- Scoped credentials and policy enforcement for every agent action
- Eval harness measuring accuracy on real cases
- Feedback loop: exceptions and overrides feed the eval set
- Audit trail: every action, input, and decision logged
- Dashboards for throughput, exceptions, and escalations
- Operations runbook: kill-switch drills, rollback, regression checks
- Cost attribution per run and per action
- Post-launch observation window on live runs
Scoped by a Feasibility Study, Fixed at Kickoff*
The study scores your candidate workflows and prices the first build. The build runs weekly-demo sprints to a launch gate. After launch, you choose what continues.
Feasibility Study
Maps your candidate workflows, scores them on volume, risk, and data readiness, and prices the build. Ends with a go or no-go verdict and a costed estimate.
The Build
Scope, timeline, and fee locked at kickoff. Weekly demos on the workflow running against real cases, until accuracy and launch criteria pass.
Iterate or Hand Over
An observation window on live runs is included. Then a retainer to automate the next workflows, or a clean handover with the audit trail and runbook.
*Build pricing depends on scope and is confirmed in the feasibility study. The number is locked before work starts.
RPA Scripts, Automation Agencies, and Operator Engineers
Workflow automation splits between brittle rule engines, agencies that ship a Zapier chain, and teams that run agents in production. The difference shows up on the first exception.
The happy path, if inputs never vary.
Whatever the demo showed.
The real process, exceptions routed to humans.
The flow breaks and the queue backs up.
A new rule gets patched in.
The agent reads the variation and decides, or escalates.
Broad credentials, pray nothing misfires.
Depends on who wired the flow.
Scoped credentials, checkpoints on irreversible steps, kill switch.
Logs nobody can read.
A ticket history.
Every action logged with inputs, decision, and evidence.
Vendor benchmarks.
Portfolio screenshots.
Agents running ~500K actions/month in PCI-DSS production.
| Dimension | RPA & iPaaS | Automation Agencies | FLYTEBIT |
|---|---|---|---|
| What gets automated | The happy path, if inputs never vary. | Whatever the demo showed. | The real process, exceptions routed to humans. |
| When inputs change | The flow breaks and the queue backs up. | A new rule gets patched in. | The agent reads the variation and decides, or escalates. |
| Control | Broad credentials, pray nothing misfires. | Depends on who wired the flow. | Scoped credentials, checkpoints on irreversible steps, kill switch. |
| Accountability | Logs nobody can read. | A ticket history. | Every action logged with inputs, decision, and evidence. |
| Proof | Vendor benchmarks. | Portfolio screenshots. | Agents running ~500K actions/month in PCI-DSS production. |
Match the Tool to the Question
Workflow automation answers how a process gets executed by agents. If that is not your question, one of these fits better.
Frequently Asked Questions
What does AI workflow automation include?
Mapping the process, connecting your systems, and deploying agents that execute it end to end: reading inputs, making decisions, taking actions, and logging every step. Human checkpoints sit on irreversible or high-risk actions. The deliverable is a running workflow with an audit trail, not a Zapier diagram.
Which workflows are good candidates for AI automation?
High-volume processes with clear inputs and measurable outcomes: ticket triage and resolution, document processing and approvals, lead qualification and CRM updates, invoice and expense handling, reporting and data sync between systems. The feasibility study scores your candidate workflows on volume, risk, and data readiness.
How is this different from Zapier or RPA?
Rule-based automation breaks on variation; agentic automation handles it. Our agents read unstructured inputs, decide between paths, call your tools through governed APIs, and escalate when a case falls outside the policy. Checkpoints and an audit trail make every action reviewable, which traditional RPA and iPaaS tools do not provide.
What happens when an agent makes a mistake?
Mistakes are contained by design: scoped credentials limit what an agent can touch, checkpoints gate irreversible actions, and every step is logged. Low-confidence cases route to a human with full context. Failures feed the eval set, so the next release handles that class of case.
How long does it take to automate a workflow?
A single workflow typically ships in 3 to 8 weeks depending on system integrations and risk profile. The governance, checkpoint, and observability infrastructure already exists from our own products, so builds start from proven components. The exact window is set in the feasibility study.
How much does AI workflow automation cost?
Cost depends on scope: number of workflows, system integrations, and governance requirements. The feasibility study, which starts from $2K, produces a costed estimate. The build price is then fixed at kickoff, so the number is agreed before work starts.
What happens after the automation launches?
Every build includes an observation window where we watch live runs, tune thresholds, and expand coverage. After that, you can keep us on a retainer to automate more workflows, or we hand over to your team with the runbook, dashboards, and audit trail.
Automate the Process, Keep the Keys
Schedule a 30-minute working session with our expert team. We will look at the workflows eating your team's time and give you a straight answer on which ones are worth automating first.