Governing AI agents with human wisdom

The real risk is meaning drift, not slow agents

A customer says, "I was not sure if my setup had saved." By the time that becomes a card it may read "Improve onboarding completion," and by the time an agent runs it the task may become: remove two steps, add an animation, update tests, open a pull request.

The change can be clean and the tests can pass while the point is lost. The customer was asking for reassurance, not fewer steps. That is meaning drift, and agents make it sharper because they act before the organization has finished sensemaking.

Human sensemaking runs through the whole loop

Wisdom is not a single approval click at the end. It is sensemaking at every handoff, kept attached to the card so an agent, and the next human, can act against the actual intent.

• Customer sensemaking explains what mattered in lived experience, not just what was requested.
• Manager sensemaking clarifies intent and the tradeoff being made.
• Team sensemaking surfaces risk, blockers, and implementation judgment.
• Delivery sensemaking asks whether the outcome matched the reason for doing the work.

When these stay linked to the card as evidence, an agent drafts from intent instead of from a flattened ticket, and a reviewer has something real to review against.

Decisions sequenced by economic priority

Evidence tells you what matters; economics tells you what matters first. ScrumDo prices the cost of waiting so agent effort and human attention go to the work that gets more expensive the longer it sits, not the work that is merely loudest.

Because priority is explicit, an agent can be pointed at the highest-economic-priority work with the tradeoff visible, instead of optimizing whatever was typed most recently.

Decision-readiness: Evidence Gaps before commitments

Before a release or epic is committed, by a person or an agent, ScrumDo checks whether it is actually decision-ready. An Evidence Gap is an explicit check with a severity and a primary action, not a vague warning.

• Release readiness: is there an owner, linked delivery work, and a parent epic?
• Story evidence: is there customer, intake, process, Strategy Feedback, and Employee Experience evidence behind it?
• Release economics: is the cost of delay and economic priority complete?
• Dependencies and budget: are blockers cleared and the epic budget coherent?

Each gap routes to its fix; attach the missing stories, complete the economics, clear the dependency, or link the epic, so neither a person nor an agent is asked to execute against evidence that is not there yet.

Governance: what the agent may see, do, and spend

BYOA is a governance model, not just a cost model. If teams bring their own agents, they must decide what each agent can see, change, and spend, and what must be logged.

1. Set agent profiles, skills, and context per room so the agent starts from the right record, not a hidden chat.
2. Require an accepted spec before execution; reviewers check it against the attached evidence and intent.
3. Run in a scoped environment with connectors whose sensitive actions stay approval-gated.
4. Set minimum and maximum token limits per agent and track token use by person so spend stays visible.
5. Let a QA agent review output, then create commits and pull requests with the record still attached.

Repeatable, governed work across many teams

Governing one agent on one card is the start. The harder win is running the same governed work across many teams without losing control. In ScrumDo a kind of work can be defined once, a work type with its task map of default steps, and provisioned into every team that needs it, so a maker–verifier loop runs the same shape of work the same way wherever it lands.

• Define a work type and task map once; clone it into many governed team workspaces, kept in sync.
• A governed loop routes steps to configured agent roles, such as maker and verifier, bounded by token, cost, and connector limits.
• Each run’s evidence and outcomes roll up to the portfolio, so learning compounds across teams instead of staying on one board.

Building trust at scale

Trust does not come from slowing agents down. It comes from making their work legible: every draft, approval, run, and review stays on one operating record that humans and auditors can read months later without reconstructing it from memory.

This is a loop of loops, customer signal, product intent, team judgment, agent execution, QA, and outcome review, each one governed and connected. As more agents and more teams join, the record is what keeps wisdom in charge.