Signs you need this
- Staff copy data between three systems by hand every day.
- A process has clear rules but too many exceptions for classic RPA.
- You want an assistant that can act, not just answer, and you're unsure how much autonomy is safe.
Overview
Automation used to mean brittle scripts and RPA. Today it means tool-calling agents that can read a ticket, look up a record, decide, and act, combined with workflow platforms that connect your CRM, ERP and SaaS tools without custom glue code.
We design multi-agent systems with clear responsibilities, escalation paths and audit trails, and we pair them with n8n, Zapier or Make where a no-code workflow is the faster, more maintainable answer.
How this differs from Generative AI Engineering: Agents act on systems with tools, approvals and audit trails; Generative AI Engineering focuses on answering, summarising and generating.
What we deliver
- Agent designRoles, tools, memory, handoffs and stopping conditions defined before any code is written.
- Multi-agent orchestrationLangGraph-based workflows with supervisors, specialists and human-in-the-loop checkpoints.
- Tool integrationsSecure connectors to your APIs, databases and SaaS tools with scoped permissions.
- No-code automationn8n, Zapier and Make workflows for event-triggered business processes.
- ObservabilityTraces for every agent decision and tool call so you can debug and audit behaviour.
- GuardrailsApproval gates, spend limits, rate limits and rollback paths for actions with consequences.
Where it fits
Back-office operationsDocument collection, KYC calls, notifications and status updates orchestrated end to end.
HR and internal service desksAn assistant that answers policy questions and routes requests into the right systems.
Decision-support agentsConversational analysis over operational data for planning teams.
Claims and case routingFlag, enrich and route cases to the right people, syncing outcomes back automatically.
Technology we use
Chosen per project. We are vendor-neutral and will recommend what fits your constraints.
LangGraphLangChainAnthropic ClaudeOpenAIGeminin8nZapierMakeRedisPostgreSQLKafka
How we work
Process mapping
Document the workflow as it really happens, including exceptions and who signs off.
Autonomy design
Decide what the agent may do alone, what needs approval and what stays manual.
Build & simulate
Implement agents and workflows, then run them against historical cases before going live.
Supervised rollout
Shadow mode, then gradual autonomy with dashboards for the people who own the process.
Typical first engagement
A process-mapping workshop and a supervised pilot on one workflow, run in shadow mode against real cases before it acts on its own.
Common questions
Code or no-code?
Both, and often together. No-code platforms are excellent for integration plumbing; custom agents handle judgement, language and complex branching.
How do we keep agents from doing something wrong?
Scoped tool permissions, approval steps for consequential actions, spend limits and full traces. Autonomy is expanded only as trust is earned.
Can agents work with our legacy systems?
Usually. If there is an API, a database or even a UI we can automate, we can give an agent a safe tool to use it.
