Service · typically 6–8 weeks to a production-grade pilot
Workflows that finish the job, not just start it.
Classic automation breaks at the first unstructured email, the first missing field, the first exception. Agentic workflows put a language model to work across your ERP, CRM, ticketing and email: it reads, decides and acts, and people approve what matters. We map, build, evaluate and run them so they hold up in production.
What an agentic workflow is
Rule-based automation follows a script. An agentic workflow is given a goal, tools and limits, and works out the steps, including the messy ones.
Robotic process automation (RPA) and classic workflow tools work when every input looks the same. Business processes rarely oblige: an order arrives as a PDF one day and as three emails the next, a supplier changes its invoice layout, a ticket describes two problems at once. Every variation used to mean a person, or another rule.
An agentic workflow puts a language model in the loop as a worker, not a chatbot. It reads the input, extracts what matters, checks it against your systems, decides the next step and calls the right tool: create a sales order, update a CRM record, draft a reply. Where a decision carries risk, it pauses and asks a person.
The deterministic parts stay deterministic. Posting rules, validations and calculations remain code; the model handles reading, judgement and exceptions. That combination lets a workflow cover the whole process instead of the easy half. Our article what is agentic AI explains the concept in depth.
- FormatProcess mapping, design, build, evaluation, rollout, monitoring
- DurationTypically 6–8 weeks to a production-grade pilot
- ForCOOs, CFOs, department heads, process owners, IT leads
- OutcomeA running workflow with KPIs, logging and human checkpoints
Workflows we automate most often
Illustrative examples. Each is document- or communication-heavy, follows a stable pattern, carries exceptions that break classic automation and has a measurable outcome.
Invoice and accounts-payable processing
Invoices arrive by email, portal or scan. The agent extracts header and line data, matches it to purchase orders and goods receipts in the ERP, flags deviations, routes approvals and prepares clean invoices for posting. See finance.
Order intake from email and PDF
Orders in any format are read, checked against price lists, stock and master data, and created as sales orders. Ambiguities go back to the customer or your team as a prepared question, not a raw email. See operations.
Ticket triage and resolution
Incoming tickets are classified, enriched with customer and asset data, answered where the knowledge base allows and routed with a summary where it does not. Escalation rules and tone are yours. See customer service.
Lead qualification and routing
Inbound leads are researched, scored against your ideal customer profile, enriched in the CRM and handed to the right owner with a draft first reply and a suggested next step. See sales.
Contract intake
New contracts are captured, key terms, obligations and deadlines extracted, compared with your standard positions and filed in the DMS with a review note for legal. See legal and compliance.
Monthly reporting
Figures are pulled from ERP, CRM and spreadsheets, checked for anomalies against prior periods, drafted with commentary and sent to the owner for review before distribution.
From process map to production
Process mapping
We sit with the people who run the process and map it as it actually happens: inputs, systems, decisions, exceptions, hand-offs. We record the baseline: volume, cycle time, error rate, hours spent.
Workflow design
We decide which steps the agent handles, which tools it may call, where it must ask a person and what it must never do, and choose the orchestration platform your team can maintain.
Build and integration
We connect the agent to your systems through APIs, the platform's connectors or the Model Context Protocol (MCP), build the workflow and the approval interface, and run it against historical cases.
Evaluation
Before anything touches live data, the workflow is scored on real, anonymised cases: accuracy, exception handling, cost per run. We fix what fails and document what stays manual.
Rollout with humans in the loop
Live operation starts with every action reviewed. As the numbers hold, review moves to sampling and exceptions. Your team learns to read the logs and adjust prompts and rules.
Monitoring and hand-over
Dashboards for volume, success rate, escalations and cost, alerts for failures, a runbook for your team. Or we keep running it under managed AI operations.
Built for reliability
Human-in-the-loop by design
Approval steps are part of the workflow, not an afterthought. Payments, customer-facing messages and master-data changes wait for a person until the evidence shows they no longer need to.
Every step logged, every exception handled
Inputs, model outputs, tool calls and decisions are recorded so any case can be reconstructed. What the agent cannot handle lands in a review queue with a summary and a proposed action, never in a dead end.
Measured against the baseline
Cycle time, cost per case, error rate, hours freed, exception and escalation rates. We set the numbers before the build and report them after; the exception rate is a KPI we drive down release by release.
Permissions the agent cannot exceed
Agents get their own identities, scoped access and rate limits. An agent that processes invoices cannot read HR files, and cannot release a payment above its threshold.
Orchestration platforms and integration tools
We choose the platform per client: low-code tools for teams that maintain workflows themselves, code-first frameworks for complex logic and strict testing. Named as tools we work with and evaluate; we hold no reseller or partnership agreements.
Frequently asked questions
How is this different from RPA or a Zapier automation?
RPA and classic automation execute fixed rules on structured input and stop when the input varies. An agentic workflow uses a language model to interpret unstructured input and choose the next step within limits you set. In practice both are combined. More in agents vs. RPA vs. chatbots.
Which platform should we use: n8n, Make, Power Automate or code?
It depends on who maintains the workflow, how complex the logic is and where your data may go. Microsoft-centred companies often do well with Power Automate and Copilot Studio; teams with developers and strict testing needs with LangGraph or similar; n8n is a strong middle ground that runs on your own EU infrastructure. We recommend after the mapping, not before.
How do agents connect to our ERP and CRM?
Through the systems' APIs, the connectors of the orchestration platform, or MCP servers that expose your systems as tools an agent can call with defined permissions. Where a system has no interface, we look at exports, database views or, as a last resort, controlled UI automation. See MCP explained.
What happens when the agent gets something wrong?
It will, occasionally, which is why the workflow is designed around that fact. Risky actions wait for approval, every step is logged, exceptions land in a review queue, and the evaluation set grows with every failure so the same mistake is caught next time. We measure the error rate against the human baseline, not against perfection.
How do we measure success, and who owns the workflow afterwards?
Before the build we record the baseline: volume, cycle time, hours spent, error and rework rates, cost per case. After rollout the dashboards report the same numbers plus exception and escalation rates, model cost and user feedback. The workflow belongs to your team, with runbook, training and evaluation set; if you would rather not operate it, managed AI operations does.
Related services
Custom AI agent development
Single- and multi-agent systems designed and built for production: tool use, memory, MCP servers, evaluation suites, guardrails and EU deployment.
Data foundations for AI
Pragmatic, incremental data work for agents: inventory, document pipelines, APIs, search, definitions and access control, one use case at a time.
Managed AI operations (AgentOps)
Monitoring, evaluation, model upgrades, cost control and incident handling for agents and LLM applications after go-live, with a monthly review and documentation your auditors can read.
AI agents for operations & supply chain
Order intake from email and PDF into the ERP, document processing, supplier follow-ups and exception handling, with people deciding on every deviation.
Let's find the first workflow worth automating.
A 30-minute intro call, no slides and no obligation. We listen, ask about your processes, and tell you honestly where AI agents would pay off and where they would not.