AI Agents and Automation
Agents that complete real work across your systems, with a person able to stop any step.
Why It Matters
A chat assistant can explain a task, but the work still waits for someone to open the systems and do it. The cost sits in the steps between systems: check a record, update the order, tell the customer.
Giving a model open access is not an option. Every action must be allowed, recorded and reversible. A person must be able to take over before a mistake reaches a customer or a ledger. Most agent projects fail for three reasons. The work is too broad to test, the tools are too open, or nobody watches the agent after launch.
What We Do
Multi-Agent Workflows With Human Oversight
Agents carry a request through several steps and stop for a person at the decisions you name.
Agents That Use Your Systems Safely
Agents act through tools we define, each with its own permissions, limits and a log an auditor can read.
Agent to Agent and Payment Protocols
Agents hand work to each other across systems through open protocols, with payments held inside limits you set.
Running and Monitoring Agents
We watch runs, catch failures and keep the record, with a queue for exceptions and a control that stops an agent.
What Changes for You
Work Completed, Not Described
Agents carry a request through the systems it touches and finish the steps that follow a rule.
Every Action on Record
Each tool call is named, permissioned and logged, so an auditor can see what the agent did and why.
People in Control
Your team sees the queue, approves the risky steps and stops any agent without losing the work.
How the Work Runs
Agents are built one workflow at a time. Each stage leaves your team with a decision and an artefact they can inspect.
Choose the Workflow
We pick one process with a measurable result, such as onboarding a supplier. You name the steps that may be automated and the steps that need a person.
Define the Tools
We expose only the systems and actions the agent needs. Your security team approves each permission, and we agree the limits on amounts, records and timing.
Build and Test
We build the agent and a set of real cases with expected results. You agree the pass mark, and the agent runs in a test environment until it meets it.
Set Up Supervision
We give your team a view of every run, alerts on exceptions and a stop control. Your staff decide which exceptions they handle and which the agent retries.
Release and Extend
We release one workflow at a time and review the early runs with your team. A new step is added only after the current one is stable.
Review the Record
We review a sample of runs with your team and turn failures into test cases. You decide which changes reach the workflow.
Platforms and Technologies
Agents work through open protocols and the libraries your engineers already use. Google Cloud, Microsoft and Salesforce are the platforms where the agents and their tools run. NVIDIA GPUs are the processors that run any model you host yourself. The Model Context Protocol (MCP) gives an agent a defined set of tools with named permissions. Agent2Agent (A2A) lets an agent in one system hand work to an agent in another. LangChain and LlamaIndex are libraries for building the agent and connecting it to documents. FastAPI is a Python framework we use to expose a tool as a small, testable service.
- Frameworks
- FastAPI
- AI and machine learning
- LangChain
- LlamaIndex
- Methods We Use
- Human supervision of agent runs
- Evaluation with real cases
- Input and output checks
- Model use and cost limits
Naming a product does not mean a partnership with its vendor.
Google Cloud
We build data, analytics and AI systems on Google Cloud, with BigQuery, Vertex AI and GKE under one control model.
Microsoft
We join Azure, Microsoft 365 and Dynamics 365 on one identity model, so teams share one set of records.
Salesforce
We build Salesforce around the agreed customer process, with clean data and every integration owned.
NVIDIA
We size and build GPU estates on NVIDIA for training and inference, then prove the stack under a real workload.
Industries
Banking and Financial Services
Banking, payments, lending, wealth, insurance and digital wallets: the platforms they run on and the connections between them.
Logistics and Distribution
Warehouse, transport and carrier integrations, tracking and proof of delivery, joined to your ERP and finance.
Public Sector
Citizen portals, case management, payments and integrations with government registries, designed to recognised accessibility guidelines.
Questions
What buyers ask before they let an agent act in their systems.
How do you stop an agent doing something it should not?
The agent can act only through tools we define, each with its own permissions and limits. Every call is logged, risky steps wait for a person, and a stop control ends a run without losing its record.
How much do people need to watch?
Agents run the routine steps and raise the exceptions. Your team sees a queue, not a stream of every action, and decides which cases need a person. We agree that split before the build.
How do you know the agent is accurate?
We build a test set from real cases with the expected result. Every change to a prompt, model or tool is scored against it, and the agent is not released below the pass mark you set.
What drives the cost?
The number of runs, the steps in each run and the model each step uses. We send routine steps to smaller models, cache repeated work and report cost per completed case.
Who owns the agents?
You own the agent code, the tool definitions, the prompts and the test set we build for you. Commercial model licences stay with their providers, and we list them in the handover.
Tell Us What You Need to Run, Build or Secure.
Describe the service, the systems and the pressure you are under. The right team takes it from there.