| name | ltai - Long-Term AI Orchestration |
| description | Plan for AI-agent coordination and autonomous operation. Design handoff protocols, error recovery, and human oversight. |
| output | {"format":"prose"} |
Long-Term AI Orchestration
Input: $ARGUMENTS
Step 1: Identify Tasks Suitable for Agents
Audit the work described in the input and classify tasks by automation suitability.
AGENT SUITABILITY MAP:
FULLY AUTOMATABLE (agent can do end-to-end):
- [Task]: [Why it's suitable — deterministic, well-defined, low-stakes]
AGENT-ASSISTED (agent does most, human reviews):
- [Task]: [What the agent does vs. what the human checks]
HUMAN-LED, AGENT-SUPPORTED (human drives, agent helps):
- [Task]: [What support the agent provides]
HUMAN-ONLY (do not automate):
- [Task]: [Why — judgment, stakes, ambiguity, relationships]
Rules:
- Tasks with clear inputs, clear outputs, and verifiable results are most suitable
- Tasks requiring taste, political judgment, or novel reasoning stay human-led
- "Fully automatable" does not mean "no oversight" — it means the agent can execute without mid-task human input
- Be conservative — overestimating AI capability is the most common error
Step 2: Design Agent Handoff Protocols
For each automatable or agent-assisted task, define how work flows between agents and humans.
HANDOFF PROTOCOLS:
1. [Task/workflow name]:
- Trigger: [What initiates the agent]
- Input format: [What the agent receives]
- Processing: [What the agent does, step by step]
- Output format: [What the agent produces]
- Handoff point: [When/how it returns to human or next agent]
- Quality gate: [How to verify the output before accepting]
2. [Task/workflow name]:
...
Rules:
- Every agent task needs a defined trigger and a defined endpoint
- Output format must be specified — agents that produce unstructured output create downstream chaos
- Quality gates are mandatory, not optional
- If two agents hand off to each other, specify the interface contract between them
Step 3: Plan for Error Recovery
Define what happens when agents fail, produce bad output, or encounter edge cases.
ERROR RECOVERY PLAN:
1. [Failure mode]: [What goes wrong]
- Detection: [How you know it failed]
- Automatic recovery: [What the agent tries first]
- Escalation trigger: [When it stops trying and alerts a human]
- Rollback procedure: [How to undo damage if any]
- Prevention: [How to reduce this failure's frequency]
2. [Failure mode]:
...
Rules:
- Assume agents WILL fail — the question is how gracefully
- Silent failures are worse than loud failures — detection is the first priority
- Automatic recovery should have a retry limit — infinite loops are a real risk
- Every escalation must have a clear human recipient, not just "someone"
Step 4: Design Human Oversight
Define the oversight structure that keeps humans informed and in control.
OVERSIGHT STRUCTURE:
- Monitoring dashboard: [What metrics/status to display]
- Review cadence: [How often humans review agent output — daily/weekly/per-batch]
- Kill switches: [How to stop agents immediately if needed]
- Audit trail: [What gets logged and where]
- Authority levels: [Who can start, stop, modify, or override agents]
Rules:
- Oversight must be proportional to stakes — high-stakes tasks need more frequent review
- Kill switches must work immediately, not "after the current batch"
- Audit trails are non-negotiable — you must be able to reconstruct what happened and why
- Avoid "oversight theater" — dashboards nobody looks at don't count
Step 5: Project Capability Evolution
Forecast how agent capabilities will change and plan accordingly.
CAPABILITY EVOLUTION:
SHORT-TERM (next 6 months):
- Agents can likely handle: [tasks moving from human-led to agent-assisted]
- Key enabler: [what makes this possible]
MEDIUM-TERM (6-18 months):
- Agents can likely handle: [tasks moving from agent-assisted to fully automated]
- Key enabler: [what makes this possible]
LONG-TERM (18+ months):
- Speculative capabilities: [what might become possible]
- Preparation needed now: [what to build/design now to be ready]
ASSUMPTIONS TO MONITOR:
- [Assumption about AI progress]: [How to tell if it's wrong]
Rules:
- Short-term forecasts should be conservative and based on current trajectory
- Long-term forecasts should acknowledge high uncertainty
- "Preparation needed now" is the actionable output — what architectures, data, or processes to build today
- Monitor assumptions actively — revise the plan when assumptions break
Integration
Use with:
/hpat -> Use historical patterns to calibrate AI capability forecasts/de -> Turn the orchestration plan into a structured delivery/mtnw -> Identify new domains where AI agents create the most leverage/immg -> Find immediate gaps in current agent infrastructure
