Agentic Systems
Design how humans, agents and tools should actually work together.
We help companies move from scattered AI experiments to an operating architecture: workflows, agent roles, human authority, controls, integrations and the first production proof.
Duration and price are confirmed in writing before anything starts.
A — When this becomes urgent
The experiments worked. The operating model did not follow.
- AI pilots exist, but production ownership is unclear.
- Teams are building agents independently, with no shared authority model.
- Legal and security want controls; the product team needs speed.
- Leadership cannot tell which workflows create measurable operating leverage.
B — What we design
Eight artifacts, all of them operable.
Workflow and decision map
How work actually moves today, where the decisions sit, and which steps carry consequence.
Agent opportunity map
Which steps are worth delegating to a machine, which are not, and what changes if they are.
Agent roles and orchestration architecture
What each agent is responsible for, how they hand off, and who arbitrates when they disagree.
Human authority matrix
Who may act, under whose mandate, inside which limits — and where a human must remain in the loop.
Tool, data and integration map
Which systems the work depends on, what each agent may read or write, and where the gaps are.
Control, escalation and revocation architecture
How a wrong action is caught, escalated and reversed, and how authority is withdrawn immediately.
Evaluation and economics model
How quality is measured and what the workflow costs per run, so leverage is arguable rather than asserted.
First-proof specification and 90-day sequence
The one production workflow to prove first, the evidence it must produce, and the order of moves after it.
What the work actually looks like.
Three plates from a specimen intervention. No client is named and no client material is reproduced — this is the method, drawn on a workflow we can show.
A high-consequence back-office workflow: an inbound customer obligation is interpreted, priced against policy, and either settled or escalated.
Intake
The obligation arrives through mail, portal or partner feed. Normalised into one record with its source and timestamp.
Interpretation
Documents read, entities resolved, obligation classified against the policy taxonomy. Confidence recorded per field.
Policy match
The classified obligation is priced against written policy. Ambiguity is surfaced rather than averaged away.
Decision
Settle, adjust or refuse. This is where consequence sits: money moves, or a customer is told no.
Execution
Settlement instructed, ledger written, customer notified. Every action carries the mandate it acted under.
Review and learning
Sampled review of decided cases. Disagreements feed the policy taxonomy, not the model alone.
Specimen intervention. An I.O.V worked example of the method — no client is named and no client material is reproduced. Figures are illustrative thresholds, not measured outcomes.
Who may act, under whose mandate, inside which limits — and where a human must remain in the loop.
Intake agent
Standing mandate · read-only outside its own record
Pricing agent
Standing mandate · bounded by the written threshold
Case operator
Role mandate · accountable named human
Reviewer
Sampling mandate · may reverse a decided case
System owner
Change mandate · cannot decide an individual case
- Settlement above the written threshold is never agent-held, whatever the model's confidence.
- No actor may both decide a case and change the rule that decided it.
- Every agent action is written with the mandate it acted under; an action without a mandate is void.
- DetectThreshold breach, low confidence, policy conflict or reviewer disagreement raises the case.
- HoldThe action is stopped before effect. Nothing settles while a case is held.
- EscalateRouted to the named operator with the record, the confidence and the mandate that was claimed.
- ReverseA decided case is unwound: ledger corrected, customer re-notified, reversal filed against the original mandate.
- RevokeThe agent's mandate is withdrawn immediately and its queue reverts to human hands. Revocation needs one action, not a release.
Specimen intervention. An I.O.V worked example of the method — no client is named and no client material is reproduced. Figures are illustrative thresholds, not measured outcomes.
C — First proof
Don't scale the assumption. Prove it.
The smallest production workflow — or single governed agentic capability — that can validate the operating architecture in real conditions.
Before agents are rolled across the organisation, the critical assumption has to be tested where the consequence is real but the exposure is small.
Implementation duration and cost are scoped after the architecture, in writing.
The one workflow proved first, written so it can pass or fail on evidence rather than on enthusiasm.
- Workflow under test
- One obligation class, one region, one queue — the narrowest slice where the consequence is real and the exposure is small.
- Assumption under test
- That interpretation and policy matching can be agent-held at production quality while the settle-or-refuse decision stays human-held.
- Evidence it must produce
- Per-case: classification agreement against reviewer judgement, cost per run, time to decision, escalation rate and every reversal with its cause.
- Pass line
- Agreement at or above the written threshold, no unmandated action, and every escalation caught before effect — held across a full review cycle, not a good week.
- Fail line
- One settlement without a mandate, or a reversal that could not be traced to its decision, ends the proof regardless of the aggregate numbers.
- Controls in place before day one
- Written thresholds, hold-before-effect on every agent action, sampled reviewer queue, and one-action revocation.
Instrument and shadow
The workflow is mapped as it actually runs, controls are stood up, and agents run in shadow with no effect on live cases.
Governed production
Agent-held steps go live inside written limits. Every decision stays human. Evidence accumulates against the pass line.
Argue the leverage
Quality and cost per run are read against the pass line, and the next workflow is scoped — or the proof is stopped and said so.
Specimen intervention. An I.O.V worked example of the method — no client is named and no client material is reproduced. Figures are illustrative thresholds, not measured outcomes.
D — Research and operator laboratories
We do not only advise on emerging systems. We operate laboratories that test them.
- NIVQRA
- A control layer for delegated machine authority: who may act, under whose mandate, inside which limits, with what evidence and with what path to revoke.I.O.V Lab · Product thesis & first proof · 2026–PresentRead the field note →
- Signal Atlas
- Contextual intelligence and AI-assisted discovery, run as an active I.O.V operator laboratory rather than a finished enterprise product.V1 live · V2 in build
- Tadle
- White-label AI product infrastructure and sandbox framework for teams shipping agentic products.Operating · framework in build
Where are agents beginning to act inside your company?
Bring us the workflow, decision or operating constraint. We will tell you whether an intervention is the right instrument.