Executive Outcomes
Advisor → Executor
From suggested fixes to gated automation
KPIs
MTTR, CFR, change lead time, SLO conformance
Governed
Evidence packs for CAB; rollback readiness
Learn
Post‑change reflection continuously improves agents
1) Deep Theory — Architectures, Planning & Safety
Planner (LLM/LRM)ExecutorsWorld ModelSafety LayersHuman‑in‑the‑Loop
- Planning: CoT/ToT/GoT to explore alternatives; use value functions (risk, blast radius, SLO deltas) to choose plans. fileciteturn4file2
- World Model: IP Fabric’s snapshots + twin approximate environment dynamics for counterfactuals.
- Safety: Policy constraints (intent checks), execution sandboxes, rollback, and evidence‑first gating.
- Evaluation: Task success, faithfulness, decision latency; A/B policies and ablations.
2) Governance in Practice — A2A • ACP • MCP
Approval Policy (excerpt)
Stages: shadow → suggest → HITL‑approve → low‑risk auto → broad auto
Gates: intent pass, twin what‑if within SLOs, blast_radius ≤ threshold, rollback present
Evidence: citations to intents/snapshots, diffs, test matrix, risk score
Pseudo‑code: Governed Execution
function GOVERNED_EXEC(plan):
sim = twin.whatif(plan.cfgs)
pass = verify.intents(sim) && sim.risk_score <= limit
if not pass: return "REJECT", evidence(sim)
stage = policy.current_stage()
if stage in ["suggest","HITL-approve"]:
return request_approval(plan, sim)
if stage == "low-risk auto" and plan.category == "low":
return execute_with_rollback(plan)
return "HOLD", "needs broader approval"
3) Adoption & Org Readiness — People + Process
- RACI: who approves, who executes, who audits; per‑environment roles.
- CAB Integration: agents attach evidence packs; CAB decisions feed the agent’s memory.
- Runbooks → Playbooks: codify fixes as agent steps; add tests & rollback; measure outcomes.
- Training: prompt patterns for NOC/SRE; interpreting evidence and risk scores.
Artifact: Evidence Pack (schema)
{ "change_id":"...","intent_results":[...],"twin_diff":"url","tests":["pre/post"],"risk_score":0.23,"rollback":"url","approvals":[{"role":"CAB","ts":"..."}] }
4) Evaluation & KPI Pack — Measure, then scale
- Pipeline metrics: Context Precision/Relevancy, faithfulness, answer relevance. fileciteturn4file2
- Ops metrics: MTTR, CFR, change lead time, post‑change incident rate.
- Adoption metrics: % changes with evidence packs, % auto‑approved low‑risk changes, agent suggestion acceptance rate.
Scorecard (example)
{ "period":"Q4","mttr_delta":"-42%","cfr":"-28%","auto_low_risk":"31%","faithfulness":"0.94","context_precision":"0.88" }
5) Executive Playbooks (No code)
Playbook A — “Auto‑Remediate Low‑Risk”
- Scope: access‑layer interface flaps; strict rollback & tests
- Stage: low‑risk auto; nightly window
- Outcome: CFR↓ without CAB overhead
Playbook B — “CAB‑Ready Major Change”
- Scope: BGP policy change
- Stage: HITL‑approve; twin evidence + risk score
- Outcome: faster, safer approvals
Playbook C — “Continuous Compliance”
- Scope: crypto strength, SNMPv3, AAA
- Stage: suggest → batch fixes
- Outcome: audit‑ready posture
6) 90‑Day Adoption Plan
Days 0–30
- Shadow mode; collect evidence packs
- Define low‑risk catalog + rollback library
- Baseline KPIs
Days 31–60
- HITL‑approve for medium risk
- Auto‑remediate low risk
- Weekly CAB with agent evidence
Days 61–90
- Expand auto scope with SLO gates
- Quarterly KPI review
- Refine playbooks via post‑mortems
Week 6 Deliverables
- Governed execution policy (A2A/ACP/MCP aware) + pseudo‑code
- Evidence‑pack schema & CAB integration template
- KPI scorecard + evaluation metric pack
- 90‑day adoption plan + three playbooks