UNCODE, an intelligence solutions company
Agentic Applications

AI agent engineering, from first design to custom applications.

UNCODE designs and engineers AI agents, multi-agent workflows and custom AI applications around an outcome your business can measure.

Let's talkHow UNCODE builds

Agent engineering

Five areas of engineering, one outcome in view.

UNCODE, an Adelaide AI consultancy, provides AI agent engineering: the design and build of AI agents, multi-agent workflows and custom AI applications around a business outcome you can measure. UNCODE sets each agent's purpose, model, prompts, tools and approvals, then connects it to the knowledge you approve, on leading enterprise AI platforms.

  • Agent designPurpose, owner, model and behaviour, set for one job
  • Multi-agent workflowsSpecialised agents that hand work to each other
  • Prompt and tool architectureClear instructions and tools with scoped access
  • Knowledge and retrievalAnswers drawn from the sources you approve
  • Custom applicationsScreens your people use to work with the agents

What UNCODE engineers for each AI agent.

Agent design

Before an agent touches real work we set its purpose, its owner, the model it runs on and how it behaves, so it does one thing well and can be measured on it.

Multi-agent workflows

Workflow orchestration across several agents, with a person in charge. A router sends each line the right way, specialised agents hand work on, and a named person approves exceptions.

Prompt and tool architecture

Every agent gets a structured prompt and a short list of tools, each with its own access scope. Where the blocks stop, a secure Python step takes over, inside the same guardrails and records.

Knowledge and retrieval

We connect the documents and systems you approve and have the agent cite what it used. Access follows the permissions your organisation already sets.

Custom applications

When the work needs its own screen, we build it, then publish it to Teams, SharePoint, web chat or a schedule.

Change control

Agents improve as the work changes. Each version is recorded, reviewed and released on purpose, so nothing drifts quietly. See how we run agents after launch under Operations.

How we run agents after launch

Agent design

AgentMonth-end match agent
Purpose

Match the ledger to bank and supplier files, and explain every gap

OwnerFinance lead
ModelClaude
Temperature0.3
Reasoning effortLowMediumHigh
Data sourcesLedger filesFinance policies

Swap the model later without rebuilding the agent.

Platforms and integrations

Connected to the systems you already run.

Agents are engineered on enterprise AI platforms and published where your people already work.

  • ClaudeClaude
  • Microsoft AzureMicrosoft Azure
  • SharePointSharePoint
  • Microsoft TeamsMicrosoft Teams
  • Microsoft ExcelMicrosoft Excel
  • XeroXero
  • MYOBMYOB
  • HubSpotHubSpot
  • SalesforceSalesforce

How UNCODE builds

How an AI agent workflow runs, from trigger to output.

TriggerStart from the workA schedule, a new file, a form or a message.
KnowledgeApproved sourcesFiles and systems the agent may read, with access following your permissions.
ModelThe right modelChosen for the job and swapped later without a rebuild.
ToolsScoped toolsBuilding blocks and secure Python, each with its own access scope.
ApprovalA named personAnything consequential waits for sign-off.
OutputWhere people workTeams, SharePoint, the web or a schedule.

See the full build process

We build it with the people who know the work

How UNCODE builds an AI agent, in six steps.

UNCODE, an Adelaide AI consultancy, builds AI agents for business workflows in four stages: Discover, Build, Enable and Evolve. The six steps below show the work inside those stages. UNCODE maps one workflow, builds and tests the agent on real data, prepares the people who will use it, then monitors and improves it after go-live, with a person approving anything consequential.

  1. Start with the workDefine the business problem, workflow, outcome and success measures.
  2. Give it the right knowledgeConnect data, systems, documents, APIs and organisational context.
  3. Turn the brief into a workflowDesign the agent, tools, orchestration and human hand-offs.
  4. Set boundaries before it actsPermissions, guardrails, policies, identity and operating constraints.
  5. Test before it goes liveEvaluate accuracy, reliability, security, edge cases, tool use and failure behaviour.
  6. Go live, then keep improvingDeploy, monitor, evaluate continuously and optimise in production.

The path

The AI agent development process: four stages, one workflow at a time.

UNCODE maps the workflow, builds and tests the agent, and sets the guardrails. Your team knows how the work runs, approves what the agent may reach and carries it forward as AI champions.

  • DiscoverFind the right starting point. Map the workflow and prioritise by value.
  • BuildProve it in the real workflow. Prototype with real data and test with realistic examples.
  • EnablePrepare people for the change. Pilot with real users and train champions.
  • EvolveKeep improving what works. Measure, refine and expand on evidence.

Your part in the build

What your team does at each step of an AI agent build.

Four things are enough to scope a first agent: the repeated workflow, who does it, which systems it touches and who approves. If you are unsure of one, we work it out with you.

Step 1Start with the workYou name the workflow and its owner, and how you will know it is working.
Step 2Give it the right knowledgeYou approve which sources the agent may read.
Step 3Turn the brief into a workflowYou check the workflow against how the work really runs.
Step 4Set boundaries before it actsYou decide which actions need a named person to approve.
Step 5Test before it goes liveYou try it on real examples and sign off.
Step 6Go live, then keep improvingYou tell us what to improve next as the work changes.

At handover your team owns the agent, with named owners and trained champions. We stay beside you for monitoring, model management and cost tracking.

AI governance

What RunGuard does for AI governance.

RunGuard is an AI governance service operated by UNCODE, an Adelaide AI consultancy. It finds the AI already in use across your organisation, controls what agents can do as they act, and keeps a record of every decision. Your team gets the visibility, controls and records without running the service day to day.

How RunGuard governs AI: discover, enforce, evidence.

  1. DiscoverFind the AI already in use across your organisation.
  2. EnforceControl every action at runtime, not only at launch.
  3. EvidenceKeep a record of every decision, ready when someone asks.

Where RunGuard fits: AI governance by sector.

Connects to
The agents, models and data sources your organisation already uses.
Safeguards
A person approves what matters, and every action is recorded.

Questions about agentic applications.

What is AI agent engineering?

AI agent engineering is the design and build of AI agents that do real work: their purpose, model, prompts, tools, knowledge and approvals, engineered around a business outcome you can measure.

What are multi-agent workflows?

A multi-agent workflow splits one process across several specialised agents, such as one that reads files, one that matches records and one that explains mismatches, coordinated by a router and handing anything consequential to a named person.

Can we change the model without rebuilding the agent?

Yes. Model settings sit in one pane for each step, so you can swap the model, adjust temperature and reasoning effort, or switch web search on or off without rebuilding the agent.

What is prompt and tool architecture?

It is how an agent's instructions and tools are structured: a prompt with a clear role, rules and output format, and a short list of tools, each with its own access scope and approval rule.

What is the AI agent development process?

It has four stages: Discover maps the workflow, Build proves it with real data, Enable prepares the people who will use it, and Evolve measures and improves what works.

What happens in the first stage of a build?

We map the workflow, prioritise by value and agree the agent's purpose, its owner and how success will be measured, before anything is built.

What does the Build stage prove?

It proves the agent works in the real workflow, using real data and realistic examples, before anyone relies on it.

What happens after the agent goes live?

It is monitored, its models and connections are reviewed, and it is improved on evidence. Going live starts the Evolve stage rather than ending the work.

How much does an AI agent cost?

Cost depends on the workflow, the systems it connects to and who runs it after launch. UNCODE confirms scope, licensing and connectors during scoping, before building starts, so you know what you are agreeing to.

How long does AI agent implementation take?

It depends on the workflow, the number of systems involved and how much testing it needs. UNCODE sets expectations during discovery, then moves from a prototype on real examples to a pilot with real users before the agent is released.

Who runs RunGuard?

UNCODE operates it. Your team gets the visibility, controls and records without having to run the service day to day.

What does it work with?

The agents, models and data sources your organisation already uses. It sits across them rather than replacing any of them.

Bring us the outcome you need to measure.

Our team will engineer the agents and applications around it, working remotely with you, wherever you are.

Let's talkSee the agents