Task-based AI agents
Agents designed to complete specific operational or knowledge-based tasks.
Agentic AI Development for Complex
Business Workflows
From assistants to agents
A chatbot that answers questions is not the same as a system that can complete one. A true agent understands the objective, breaks it into steps, retrieves information and calls the right tools and APIs.
It can coordinate with other agents, make decisions within defined limits, and escalate to a person when approval is genuinely needed.
Tool and system integration
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CRMs
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ERP systems
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Internal APIs
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Databases
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Document repositories
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Email systems
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SaaS products
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Search platforms
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Cloud services
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Workflow tools
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Custom business applications
What we build
Multi-agent systems
Multiple specialised agents working together across different parts of a workflow.
AI copilots
Assistants embedded into business applications that help users complete complex work faster.
Research agents
Systems that gather information from approved sources, analyse it and produce structured outputs.
Workflow agents
Agents that coordinate actions across APIs, databases and enterprise platforms.
Document agents
Agents that can retrieve, analyse and reason across large collections of business documents.
How agentic systems work
A practical agentic workflow often looks like the flow below. Each stage can be controlled through policies, permissions and approval rules. The goal is not simply to make an agent autonomous — it is to give it the right level of autonomy for the task.
Agent orchestration
Research Agent
Collects and validates relevant information.
Analysis Agent
Interprets the information against business rules.
Action Agent
Performs approved actions in connected systems.
Review Agent
Checks outputs before completion.
Built with control in mind
Human control remains important
Agents can recommend an action, prepare it, or request approval before anything happens. Where the risk is low, they can execute within defined limits, escalating anything unusual to a person.
Agent permissions and authority
We define exactly what each agent can access, which tools and actions it's allowed to use, and what needs human approval first. Every action is logged, and access can expire or change as the workflow evolves.
RAG and trusted knowledge
Agents reason from your approved knowledge, not guesswork: policies, contracts, procedures, technical documentation, product information and regulatory guidance, grounded through retrieval rather than memory.
Agentic AI security
Security is built around agent identity and authentication, tool permissions and action limits. We plan for prompt injection and data leakage risks, with audit trails, monitoring and clear failure handling throughout.
Our agent development
process
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01. Discover
Identify the workflow, users and business outcome.
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02. Map the workflow
Understand each decision, system and action involved.
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03. Define authority
Decide what the agent can do independently and where approval is required.
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04. Prototype
Test reasoning, retrieval and tool usage.
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05. Integrate
Connect the agent to approved systems and data.
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06. Evaluate
Test task completion, reasoning accuracy, tool selection and failure scenarios.
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07. Deploy
Release with monitoring, permissions and controls.
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08. Improve
Refine behaviour based on real-world usage, cost and latency.
Use cases
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Customer operations
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Research
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Document processing
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Compliance workflows
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Sales operations
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Internal knowledge
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Financial analysis
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Support teams
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Software operations
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Data workflows
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Administrative processes
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Enterprise automation
Why WeUno
AI plus software engineering
We build both the AI layer and the systems it needs to work with.
Designed around real workflows
We start with the business process rather than the agent technology.
Enterprise integration capability
Agents can be connected to existing APIs, applications and data platforms.
Control by design
Permissions, approval points and monitoring are considered from the start.
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