AI agents
Design agent-like workflows around named 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.
- Define the task
A model can suggest a plan, but uncontrolled tool use creates operational risk.
- Prepare approved context
Intent → clarify → plan → approve → execute per effect → reconcile
- Evaluate the response
The agent receives only the tools and permissions required for the agreed task.
- Escalate when needed
One tool action succeeds, a later one fails, and the agent repeats the first effect.
- Record the outcome
Each proposed action is scoped, approved where needed and verified against provider state.
The straightforward path is only half the design.
The owner still needs to be visible 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.
- 02The operating owner for ai agents must be able to approve, pause or reverse its consequential step.
Failure modes
- 01One tool action succeeds, a later one fails, and the agent repeats the first effect.
- 02AI agents is not retried until the earlier provider outcome has been checked.
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 named 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 does AI agents involve?
A tool and permission map, agent workflow, evaluation set and recovery design. Scope is agreed after the current process, access and acceptance cases have been inspected.
What could prevent AI agents from being the right next step?
The goal is open-ended autonomy across systems with no accountable operator.
What proof supports this AI agents page?
The evidence below is limited to the recorded Keystone test or demonstration relevant to AI agents; its label explains what was and was not exercised.
Describe where the work around AI agents gets stuck and what should happen next.
The request keeps this page and its intent attached. A person reviews the context before any customer-facing follow-up.
Discuss this workflow