AI agents that move work forward.
We design AI agents that understand context, use approved tools, complete multi-step workflows, and involve people at the decisions that matter.
- ContextRetrieves the order, policy and account history it is permitted to see.
- PlanSequences the steps allowed for this case.
- ToolsSupport platform · payments · records.
- CheckpointA person approves the refund amount.
- ActionThe approved step is completed and logged.
- ResultCustomer informed, record updated.
- EvaluationOutcome checked, returned to context.
Not every workflow needs an agent.
Four approaches, each with a different job. The most capable one is not automatically the right one — and the simplest option that solves the problem is usually cheaper to run and easier to trust.
Best when the steps and conditions are known in advance. The path is fixed, so behaviour is fully predictable and cheap to run.
the input is unstructured, or the exceptions outnumber the rules
- Nothing — you did
- Structured, expected
- Volume and repetition
Best when users need to ask questions, find information, or receive guidance through conversation. It answers; the person acts.
the work has to be completed in other systems, not explained
- What to answer
- A question
- Access to knowledge
Best when the system needs to interpret context, choose among approved actions, use tools, and complete a multi-step objective.
the workflow is already deterministic, or the steps are undefined
- Sequence, within bounds
- An objective
- Coordination
Relevant when separate specialised agents need to coordinate distinct parts of a complex workflow — and rarely the first thing to build.
one agent could do it; added coordination is added failure surface
- Delegation
- A composite objective
- Separation of concerns
If a rule-based automation or a well-scoped assistant answers the problem, we will recommend that instead.
Give repetitive coordination
a better system.
Eight kinds of work agents are suited to. These describe what we design — not projects we claim to have delivered.
Read incoming requests, identify intent and urgency, gather context, and route work to the correct destination.
Answer approved questions, access relevant information, complete allowed actions, and escalate sensitive or uncertain cases.
Collect information from approved sources, organise findings, and prepare structured summaries for human review.
Extract information, validate required fields, update systems, and flag exceptions.
Qualify inquiries, prepare account context, update records, and support approved follow-up workflows.
Compile information across tools, identify notable changes, and prepare recurring operational reports.
Help employees locate information, complete requests, and navigate internal processes.
Prepare recommendations and supporting context while keeping final decisions with authorised people.
An agent is only useful
when the workflow is designed.
Seven stations. A task enters at the trigger and docks into each one in sequence — and it stops at the checkpoint whenever a person needs to decide.
- TriggerA request, event, schedule, or system change begins the workflow.
- ContextThe agent retrieves the permitted information needed for the task.
- PlanThe system determines the appropriate sequence within defined boundaries.
- ToolsThe agent uses approved applications, APIs, databases, or internal services.
- CheckpointA person reviews sensitive, uncertain, financial, or high-impact actions.
- ActionThe approved step is completed and recorded.
- EvaluationThe result is checked, monitored, and used to improve future behaviour.
Lime marks orientation and the human checkpoint. Evaluation feeds back into context and planning, so the same workflow behaves better the next time it runs.
The right amount of autonomy,
not the maximum amount.
- Assist The agent gathers information and prepares work while a person completes the action.
- Recommend The agent proposes a decision or next step with supporting context.
- Execute with approval The agent prepares an action but requires authorised human confirmation before completion.
- Execute within boundaries The agent completes predefined low-risk actions and escalates exceptions.
- Business risk
- Data sensitivity
- Reversibility of the action
- Confidence in the decision
- Financial impact
- Regulatory considerations
- Organisational policy
Levels are chosen per step, not per project — one workflow often mixes all four. None is better than the others, and none of them means unsupervised operation.
Agents become useful when they can work with the right systems.
- CRM systems
- ERP and operational platforms
- Databases
- Support platforms
- Email and messaging
- Scheduling systems
- Document repositories
- Internal knowledge bases
- Approved third-party services
- Project-management tools
- Internal APIs
- Workflow and approval tools
Categories of system an agent may connect with, subject to your access rules. We show platform logos only where their use has been approved — working with a tool is not a partnership, a certification, or an endorsement.
From workflow audit to working agent.
A menu, not a checklist. Final scope depends on the workflow — no engagement includes all of it, and we do not sell fixed packages.
- Workflow audit
- Opportunity assessment
- Risk and feasibility review
- Success criteria
- Agent roles and responsibilities
- Workflow and decision logic
- Human checkpoints
- Interface and interaction design
- Exception and fallback behaviour
- Agent and orchestration development
- Tool and API connections
- Knowledge retrieval
- Permissions and access controls
- Testing environments
- Scenario testing
- Quality evaluation
- Failure testing
- Human-review testing
- Workflow observability
- Monitoring
- Feedback collection
- Workflow refinement
- New tool connections
- Ongoing evaluation
Five stages for an agentic workflow. The role of the agent is settled before anything is built, and the hardest step is prototyped before the rest.
Agent workflow in practice
Request, context, plan, human checkpoint, action, and evaluation. The lime station marks the decision that stays with a person.
- Service operations agent
- A multi-team workflow spanning customer requests, operational records, and exception handling.
- Routine requests needed context from several systems while sensitive decisions still required clear human ownership.
- Workflow design, human-AI experience design, orchestration architecture, and integration planning.
- A supervised agent retrieves permitted context, prepares the next action, and pauses for a person at defined checkpoints.
- A clearer operating model for routine work, handoffs, and exceptions without removing human control.
Designed to know its role—and its limits.
The operational controls around an agent, grouped by what they govern. These are design decisions agreed with your security, legal and operations teams — not guarantees of accuracy, security or compliance.
Working answers. Scope, timing and commitments are agreed per engagement, in writing.
A chatbot answers; an agent completes work. An assistant responds to a question and hands the next step back to you. An agent is given an objective, retrieves the context it is allowed to see, decides a sequence within defined boundaries, uses approved tools, and stops where a person needs to decide.
Multi-step work with clear boundaries, inputs that vary, and a definition of a good outcome — triage, routing, reconciliation, preparation and handoff. Work that is already fully deterministic is usually better served by rule-based automation.
Usually, through the interfaces your systems already expose — APIs, databases, event streams — under your existing permissions. Where no interface exists, we scope the alternative before committing to it.
Yes, and that is a design decision made per step rather than per project. An action can be prepared and held for authorised confirmation, recommended with its supporting context, or completed within predefined low-risk boundaries.
Scenario testing with realistic cases from your own work, including the awkward ones: incomplete inputs, conflicting information, tool failures. We test the human-review path as deliberately as the automatic one, and the evaluation criteria are agreed before the build.
It stops and hands over. Uncertainty and anything outside the designed boundaries route to a named owner with the context already gathered, and the case is recorded so the workflow design can be adjusted.
That is usually the sensible way in — one workflow, one team, with the oversight model tested at a small scale before anything widens.
Actions are logged, evaluation scenarios are re-run when the system changes, and behaviour is watched against the criteria agreed during design. Users need a way to report problems, and someone in your organisation needs to own the workflow — we set both up at handover.
Have a workflow that keeps
falling through the cracks?
Tell us which work repeats, where it stalls, and who decides what. We will help determine whether an agent belongs there.
studio@quirkydock.com · working internationally · CET