The agent has no clear job
People know they want an AI agent, but the work it should perform has not been defined clearly enough.
AI Agents
An AI agent is only useful when the workflow it supports is clear.
Changeable helps organisations design, build and govern AI agents that support real work, reduce manual effort and fit safely into existing business processes.
Workflow-first
Governed design
Practical deployment
Agent design pathway
01
Define the jobWhat work should the agent support?
02
Design the workflowWhere does it fit and escalate?
03
Govern the behaviourWhat needs review, limits or controls?
04
Deploy and improveHow will it be tested and maintained?
The problem
Most AI agents fail because they are treated like tools, not parts of a workflow.
An AI agent can answer questions, draft content, retrieve knowledge, summarise information or support decisions. But it only becomes useful when its role, boundaries, inputs, outputs and escalation points are clearly designed.
Changeable starts with the work the agent needs to support, then designs the agent around the process, information, people, governance and value that matter.
The knowledge base is not ready
Agents depend on usable information, clear sources, current content and controls over what can be retrieved or generated.
Governance is bolted on too late
Without boundaries, review rules and escalation pathways, agents can create quality, privacy, trust and accountability risks.
Types of agents we design and deploy
AI agents should be purpose-built around specific work, not introduced as generic assistants without a clear role.
Research and intelligence agents
Agents that help gather, summarise, compare and structure information for analysis, planning or decision support.
Document and data processing agents
Agents that extract, classify, summarise or prepare information from documents, forms, emails or structured data.
Customer and stakeholder communication agents
Agents that support triage, routing, draft responses, FAQs, onboarding, service information or internal communications.
Operational workflow and process agents
Agents that assist with repeatable workflows, reminders, handoffs, reporting, review steps or task preparation.
Knowledge base and advisory agents
Agents that help people find and apply internal knowledge, policies, procedures, templates or project information.
Multi-agent orchestration
Where useful, multiple agents can be designed to support a broader workflow with defined roles, handoffs and controls.
How we design and deploy your agents
A practical method for designing AI agents around real workflows, governed behaviour and measurable value.
Use case discovery and role definition
Define what the agent should do, who it serves and where it fits in the work.
- Business problem definition
- Agent purpose and role
- User and stakeholder context
- Value and feasibility assessment
Architecture and governance design
Design the agent’s workflow, boundaries, knowledge sources, permissions and controls.
- Workflow and escalation logic
- Knowledge base design
- Risk and privacy controls
- Human review model
Build, integration and testing
Build or configure the agent and test whether it behaves reliably in real use cases.
- Prompt and instruction design
- System or workflow integration
- Scenario testing
- Quality and safety checks
Deployment, monitoring and evolution
Deploy the agent carefully and improve it based on actual use, feedback and risk signals.
- Launch and adoption support
- Usage monitoring
- Improvement backlog
- Governance review cycle
Governance
Governance is built in, not bolted on.
AI agents need clear boundaries because they can retrieve information, generate outputs and influence work. Governance should be part of the design from the start.
- Define what the agent can and cannot do
- Control what knowledge sources it can access
- Design escalation points and human review
- Set quality, privacy and output standards
- Monitor performance and improve over time
- Document ownership, maintenance and accountability
What you receive
Practical outputs to help you design, build, test, govern and improve an AI agent safely.
Agent use case definition
A clear statement of the business problem, user, workflow, expected value and success criteria.
Workflow and escalation design
A practical model showing where the agent fits, when it acts and when humans need to review or intervene.
Knowledge base and source plan
A structured plan for the information the agent can use, including source quality, access and maintenance needs.
Agent instructions and behaviour rules
Guidance, prompts, boundaries and response expectations that shape how the agent behaves.
Testing and governance checklist
Scenario testing, quality controls, privacy checks, escalation rules and performance review points.
Deployment and improvement plan
A practical pathway for launch, adoption, monitoring, maintenance and continuous improvement.
Who this is for
AI agents for organisations that need useful support, not novelty demos.
This service is designed for organisations that want agents to support real workflows, knowledge work, communication, analysis or service delivery. If you are new to AI agents, Zero to AI has a practical assistant-building walkthrough.
SMBs needing leverage without growing headcount
For businesses that need to reduce manual effort, improve response quality or help teams access knowledge faster.
Councils and public sector organisations
For organisations that need governed agents to support information access, triage, internal service or community-facing workflows.
Knowledge and professional services teams
For teams that rely on research, documentation, quality review, knowledge reuse and consistent client delivery.
Organisations preparing for agentic capability
For leaders who want to explore AI agents carefully, with clear use cases, governance and implementation discipline.
Questions
Have a question about AI Agents?
Common questions before organisations design, build or deploy AI agents.
What is an AI agent and how is it different from a chatbot?
A chatbot usually responds to questions. An AI agent is designed around a specific job or workflow and may retrieve information, draft outputs, trigger steps, support decisions or escalate work to a human.
How do I know if we need an AI agent?
Start with the business problem. If there is repeatable knowledge work, communication, triage, documentation or decision support, an agent may be useful.
Can this connect to our existing systems?
Sometimes. Integration depends on the systems, data access, security requirements and workflow. The use case should be designed before the integration is chosen.
How do you manage risk?
By defining the agent’s role, boundaries, knowledge sources, human review points, output standards, privacy requirements and escalation pathways through practical AI governance.
What happens after the agent is deployed?
The agent should be monitored, reviewed and improved over time. Governance, usage feedback and quality checks should continue after launch. Some agent workflows may be built around platforms such as Anthropic Claude and n8n, depending on fit.
Ready to design an AI agent that supports real work?
Start with a use case-led conversation. We will help you clarify the workflow, value, risk and practical design before anything is built.
Changeable works in coordination with Zero to AI and MOI Labs NZ, with broader sector awareness through AI Forum New Zealand.