AI operationsAgents, automation & workflows
→Knowledge hubResources
Practical guidance for designing AI + human operations that actually work.
Playbooks for deciding what to automate, where humans should stay in the loop, how to measure quality, and how to turn customer conversations into useful operating insight.
Resource library4 learning tracks
⌕What are you trying to improve?
Human-in-the-loopDelegation & specialist support
→Customer intelligenceQA, sentiment & pain points
→Operating disciplineOwnership, controls & measurement
→Choose your starting point
Start with the operating problem you are trying to solve.
You do not need to understand every AI tool first. Start with the workload, the customer problem or the control gap — then decide what belongs in execution, control and intelligence.
01AI operations
What should an AI agent own?
Map repeatable work into tasks, rules, context, exceptions and measurable outcomes.
Explore AI workflows → 02Customer supportWhere should automation stop?
Separate repeatable support from cases that need judgement, empathy, escalation or approval.
Explore AI + human support → 03DelegationWhat still needs a human owner?
Design specialist roles around clear ownership rather than filling a generic “VA” bucket.
Explore specialist support → 04IntelligenceHow do we know the system is working?
Think beyond activity metrics toward QA, scorecards, sentiment, pain points and outcomes.
Explore the platform →Field playbooks
The questions we use when designing real workflows.
These are the practical decisions that determine whether an automation or delegated role becomes useful — or just another layer to manage.
Designing an AI agent
- What is the trigger?
- What context is required?
- What can the agent decide?
- What must escalate?
Documenting a repeatable process
- Expected input and output
- Required tools and access
- Decision points
- Exceptions and ownership
Measuring quality
- Accuracy and completion
- Response quality
- Policy adherence
- Human review signals
Learning from conversations
- Sentiment shifts
- Recurring pain points
- Feature requests
- Escalation themes
Designing access safely
- Minimum required access
- Named accounts
- Approval boundaries
- Auditability
Knowing when to expand
- Backlog is controlled
- Quality is stable
- Ownership is clear
- The first workflow earns expansion
Field guides
Go deeper on the decisions behind AI + human operations.
Practical guides for delegation, support automation, quality, conversation intelligence and agent performance.
01Delegation design
The AI Delegation Framework
What to automate, what people should own and what should stay internal.
Read guide → 02Operating modelAI agents vs virtual assistants
Where each model works best — and how to combine them without adding management overhead.
Read guide → 03Quality systemsWhat Auto QA should measure
Design a quality rubric that improves the operation instead of creating another vanity score.
Read guide → 04Customer intelligenceConversation intelligence
Turn support traffic into structured evidence about pain points, sentiment and feature needs.
Read guide → 05Human controlHuman-in-the-loop AI
Place human authority where uncertainty or impact justifies it — not everywhere.
Read guide → 06Agent performanceMeasure an AI support agent
Go beyond deflection rate to quality, escalation behaviour and customer experience.
Read guide →Best place to start
Not sure whether a workload needs AI, a person, or both?
The Workload & AI Delegation Audit helps separate the tasks that belong in automated execution, named human control, specialist ownership or your internal team.
Start the free audit →
AutomateAI agent
AssistAI + human
DelegateHuman owner
KeepInternal team
Start with the workload
Have a workflow you are trying to untangle?
Send us the recurring work, tools and bottlenecks. We can help you decide what should be automated, delegated or redesigned first.