AI agents
Design agent-like workflows around explicit tools, permissions, plans and per-action verification.
Each proposed action is scoped, approved where needed and verified against provider state.
A model can suggest a plan, but uncontrolled tool use creates operational risk.
The task is bounded and every consequential tool effect can be observed.
A tool and permission map, agent workflow, evaluation set and recovery design.
The agent receives only the tools and permissions required for the agreed task.
- Capture the event
A model can suggest a plan, but uncontrolled tool use creates operational risk.
- Resolve context
Intent → clarify → plan → approve → execute per effect → reconcile
- Apply the rule
The agent receives only the tools and permissions required for the agreed task.
- Human control
A named person reviews ambiguity or consequential action for ai agents.
- Verify the effect
Read back the important state, record exceptions and confirm the next owner.
The straightforward path is only half the design.
Ownership should remain clear when input is ambiguous, a provider only partly succeeds or a person needs to take over.
Open controls and recovery detail
Human controls
- 01The agent receives only the tools and permissions required for the agreed task.
- 02Name the person who can approve, pause or reverse the consequential step.
Failure modes
- 01One tool action succeeds, a later one fails, and the agent repeats the first effect.
- 02Unknown provider outcomes are retried without checking whether the first action succeeded.
Work you can inspect
See the system, what was exercised and the status of the evidence. Open the technical record for source, date and limits.
Workflow control prototypes
Reference prototypes have exercised event identity, partial-failure recovery, follow-up stops and deterministic reporting against documented synthetic scenarios.
Inspect technical record
- Source
- Keystone prototype 01–06 acceptance records
- Artifact
- Acceptance suites and post-build audits
- Boundary
- These are reference implementations, not customer case studies; several provider paths remained simulated.
- Freshness
- review annual · review by 2027-08-12
Sources behind the explanation
Current platform documentation and implementation principles sit here, separate from Keystone delivery evidence.
Controlled workflow design principles
Consequential workflows need explicit ownership, stable event identity, visible exception states and a defined human authority for ambiguous or irreversible actions.
Open source and scope
- Source
- Keystone engineering policy derived from implementation and acceptance-test practice
- Boundary
- These principles guide design. They do not prove a particular workflow has been deployed or will produce a commercial result.
- Freshness
- stable
What affects the scope?
What would Keystone deliver for AI agents?
A tool and permission map, agent workflow, evaluation set and recovery design. The exact boundary is agreed after the current process, access and acceptance cases are understood.
When is AI agents not the right next step?
The goal is open-ended autonomy across systems with no accountable operator.
Does this page describe a customer deployment?
Only evidence labelled Production implementation can imply a real production deployment. Reference architecture, best practice and official documentation explain the approach without making that claim.
Show us the process that keeps getting stuck.
The request keeps this page and its intent attached. A person reviews the context before any customer-facing follow-up.
Discuss your workflow