Task records store
Task records store objective, status, actor, scope, model context, permissions, created time, and last transition.
Architecture artifact
Agentic Workflow Runner Architecture
A concrete AI systems design artifact for running LLM-assisted workflows through explicit task state, approval gates, tool permissions, retries, audit events, recovery paths, and human-operable controls.
Problem
Why This Artifact Exists
The artifact turns the AI direction into a concrete system design that can be implemented, tested, and evaluated.
Workflow Reliability
LLM workflows often fail as production software because generated actions are treated as one-shot responses instead of stateful operations with ownership, approval, retry, failure handling, and auditability.
Architecture
System Boundaries
The runner is designed around durable state, approval gates, explicit permissions, execution workers, audit trails, and recovery behavior.
Workflow states separate
Workflow states separate draft, planned, awaiting approval, executing, failed, recovered, completed, and cancelled paths.
Tool permissions define
Tool permissions define which actions can run automatically and which require explicit human approval.
Audit events record
Audit events record prompts, decisions, approvals, tool calls, retries, failures, and recovery actions.
Recovery logic allows
Recovery logic allows failed tasks to resume from known state instead of restarting with hidden context.
Modules
Implementation Modules
Each module owns one part of the workflow so AI behavior remains observable, controllable, and maintainable.
Task State Store
Planner
Approval Gate
Tool Policy Layer
Execution Worker
Audit Log
Recovery Controller
Observability Panel
Data Flow
How The Workflow Moves
The most important part of the design is state movement, not model invocation.
- A user creates a task with objective, context, and allowed scope.
- The planner converts the objective into proposed steps and requested tool permissions.
- High-risk steps wait for human approval before execution.
- The execution worker runs approved actions and writes every transition to the audit log.
- Failures move the task into a recoverable state with retry limits and visible error context.
- Completed tasks expose final output, executed actions, and traceable decision history.
Evidence
Why It Fits The Portfolio
This artifact is grounded in existing systems and writing rather than being presented as a finished AI product.
The artifact is connected to current writing on agentic workflow architecture and AI workload observability.
The design reuses existing portfolio themes: workflow recovery, audit visibility, permission boundaries, background jobs, and observable system state.
It creates a concrete implementation target for future AI-assisted infrastructure and developer-productivity experiments.