AI & Agentic Systems
The best agent may be no agent.
We engineer enterprise systems that use the right mix of deterministic automation, AI-assisted judgment, bounded agentic action, human control, measurement, and governance.
How we approach the work
Three principles guide how we approach an engagement, in this order.
01
Start with how the work should operate
Do not start by asking which agent platform to buy. Define where judgment is actually required, who owns each decision, and what information or action rights cross each boundary.
02
Deterministic automation before agents
Use deterministic automation wherever the rules and state transitions can be made explicit. Introduce probabilistic reasoning only where ambiguity, unstructured information, or contextual judgment creates actual value.
03
Governance before scaling
Scaling an agent before its identity, permissions, boundaries, observability, escalation paths, and failure modes are defined is not scaling a capability. It is scaling an uncontrolled decision-maker.
Decision model
Six questions that define the system
Before deciding what should be automated, agentic, or human-led, make six things explicit.
| Question | What it forces |
|---|---|
| Who? | Actor: system, human, agent, owner |
| Does what? | Task and decision right |
| With what data? | Context, permission, provenance, sensitivity |
| When? | Sequence, trigger, latency, escalation |
| Under what constraints? | Policy, autonomy boundary, approval |
| How measured? | Accuracy, false-positive / false-negative cost, confidence threshold, economic outcome, risk |
Who?
Actor: system, human, agent, owner
Does what?
Task and decision right
With what data?
Context, permission, provenance, sensitivity
When?
Sequence, trigger, latency, escalation
Under what constraints?
Policy, autonomy boundary, approval
How measured?
Accuracy, false-positive / false-negative cost, confidence threshold, economic outcome, risk
Evaluation should reflect the cost of being wrong, not just aggregate accuracy. The acceptable confidence threshold depends on whether a false positive, false negative, or delayed decision carries the greater consequence — and should determine when the system acts, asks for review, or abstains.
GUARDRAILS — constrain before execution
Policy · Autonomy boundary · Approval
ACTOR
ACTION
DATA
TIMING
MEASURE — evaluate after execution
Accuracy · Cost · Confidence · Outcome · Risk
The practice
From initial assessment through governed scale, the practice is organized around how an enterprise AI decision actually gets made.
AI Strategy & Operating Models
Decompose how the work operates, decide what to automate, augment, or redesign, and set autonomy boundaries before choosing technology.
Agentic Systems & Automation
Bounded agent design: orchestration, verification, fallback paths, and explicit contracts at every handoff.
Enterprise Knowledge Systems
Governed access to enterprise knowledge through retrieval-grounded systems, with permissions and provenance treated as first-class design constraints.
AI Governance, Evaluation & Scale
Identity, least privilege, approval gates, and runtime monitoring — paired with measuring value: evaluation that ties task quality to cost per successful outcome, not adoption counts, and asks continuously whether a model, an agent, a deterministic rule, or a person is the lowest-cost reliable way to get the job done.
Multi-Agent Architecture
Adding agents does not add capability by default
Every additional agent is another handoff, another context boundary, another source of variance, and another unit of variable cost. For most enterprise workflows, a structured orchestrator-executor pattern is easier to bound, observe, and govern than an unstructured swarm.
Where this connects
AI-enabled SDLC and developer-workflow work is scoped jointly with Engineering Transformation, where the codebase and delivery pipeline are already the system of record.
Rovo enablement and JSM automation are workflow design problems before they are AI problems, scoped jointly with the Atlassian practice.
Start with a workflow assessment
Map the operating model, the decision rights, and the ROI — and find where AI earns its complexity before anything gets built.