Node AI.01
Agentic AI systems
Agents that plan, use your tools, and wait for a yes.
AI agents and multi-agent workflows that plan, call your APIs and take action in business systems, with human approval checkpoints and a full audit log.
The problem
A lot of operational work is a chain of small steps across systems: look something up in the ERP, check it against an email, update the CRM, draft a reply. People do it because no single tool spans all of those systems.
Chatbots can talk about the work but cannot do it. Scripts can do it but break the moment an input looks different. Agents sit between the two: they can read messy inputs, decide the next step and call real tools, as long as they are given clear limits.
What we build
- Single agents and multi-agent workflows for defined business processes: reconciliation, lead qualification, order follow-up, ticket triage, report preparation
- A tool layer that exposes your systems to the agent through narrow, permissioned actions instead of raw database or admin access
- Human approval checkpoints on any step that sends, pays, deletes or commits, with the context a reviewer needs to decide quickly
- An audit log of every plan, tool call, input and output, so you can see what the agent did and why
- Fallbacks: when the agent is unsure or a tool fails, the task goes to a person with the work so far attached
How it works
A trigger starts the run. The planner breaks the task into steps and calls tools one at a time. Anything with side effects stops at an approval gate. Every step is written to the audit log.
- Step 1Map the processWe sit with the people who do the work today and write down every step, system and decision, including the exceptions.
- Step 2Build the tool layerEach system the agent needs gets a small set of typed, permissioned actions. Read actions and write actions are separated.
- Step 3Agent and gatesThe planner, prompts and approval rules are built and tested against recorded real cases before anyone relies on it.
- Step 4Run in the shadowThe agent runs alongside the team, proposing actions without taking them, until its decisions match what people would do.
- Step 5Go live with monitoringApprovals, failure rates, cost per run and time saved are tracked from day one.
Deliverables
- Process map with the steps the agent handles and the ones it hands back
- Tool layer for the systems involved, with permissions documented
- Agent workflow with approval rules, deployed in your cloud or ours
- Evaluation set built from your real cases, and the results
- Monitoring dashboard for approvals, failures and cost per run
- Runbook and handover session for your team
Typical stack
- Python
- LLM and agent frameworks
- Google Cloud
We choose tools to fit your environment; this is where we usually start.
Ways to work with us
Questions
Will the agent take actions without anyone checking?
Only where you decide it should. By default, any step that sends a message, moves money, deletes data or commits a record stops for approval. As confidence builds you can relax specific rules, one at a time, with the evidence from the logs.
Which models do you use?
Whichever fits the task, your data policy and your budget. We design the system so the model can be swapped, and we test alternatives against the same evaluation set before switching.
Does our data leave our environment?
It depends on the deployment you choose. Agents can run in your own cloud account, and model providers can be configured not to retain or train on your data. We document exactly where data flows before building.
How is this different from RPA?
RPA follows fixed scripts and breaks when inputs change. An agent can read unstructured inputs and choose the next step, but we still use deterministic code for anything that should never vary, such as calculations and permission checks.
Talk to us about agentic AI systems for your business.
Send a short brief. We reply by email, usually with questions before we propose anything.