Executive Outcomes
↓ MTTR
Agent‑assisted RCA & verified fixes
↓ CFR
Twin‑gated execution with rollback
Audit
Every plan has evidence packs
Scale
A2A collaboration across functions
Guardrails: All actions must pass policy (Intent Verification), twin simulation, and human approval thresholds.
1) Deep Theory — Agentic AI for Networking
Planner (LLM/LRM)ToolsMemoryPoliciesHuman‑in‑the‑loop
- Reason–Act–Reflect loop: Observe (snapshot/path/intent) → Plan (decompose) → Simulate (twin) → Approve → Execute → Verify → Learn.
- Safety model: Tool allowlists, schema‑validated outputs, runbook tests, rollback readiness.
- Evaluation: Task success, time‑to‑resolution, change failure rate, faithfulness to evidence.
IP Fabric mapping: Snapshots & path = observation; Intent rules = guardrails; Twin = sandbox for counterfactuals.
2) Agent Frameworks Landscape (Top 10)
LangGraph — graph‑native agent workflows, stateful nodes, great for A2A orchestration.
LangChain Agents — mature tool abstractions, ReAct & tool‑calling patterns.
LlamaIndex Agents — data‑centric agents with strong RAG tooling and observability.
CrewAI — role‑based multi‑agent collaboration with task decomposition.
Microsoft AutoGen — agent chat loops, tool/plugin ecosystem, group chats.
Semantic Kernel — orchestration with skills/functions; C#/Python friendly.
AgentScope — scalable multi‑agent runtime oriented for production workloads.
Haystack Agents — open‑source RAG + agent pipelines with modular components.
Transformers Agents — HF tool‑use primitives for quick prototypes.
Flowise/Low‑code Orchestrators — visual builders to compose agent flows quickly.
Pick for fit, not hype: If topology reasoning & long‑running state are key → LangGraph. If data‑heavy RAG is central → LlamaIndex. If enterprise/.NET → Semantic Kernel.
3) A2A • ACP • MCP — How agents talk & stay safe
A2A (Agent‑to‑Agent)
Contract: POST /a2a/handshake
Body: { "from":"stability-agent","to":"security-agent","capabilities":["read:intents","whatif:twin"], "session_ttl_s":900 }
Returns: { "session_id":"A2A_123", "grants":["read:intents"], "expires_at":"..." }
Contract: POST /a2a/message
Body: { "session_id":"A2A_123","role":"request","type":"task","task":{"id":"TASK_9","goal":"validate change plan"},"evidence":["/packs/whatif/aa1.html"] }
Returns: { "status":"accepted","eta_s":15 }
ACP (Agent Communication/Control Protocol)
Envelope:
{
"msg_id":"...","sender":"...","recipient":"...",
"intent":"observe|plan|simulate|verify|approve|execute|reflect",
"payload":{...},
"policy":{"allow":["tools:read_only"],"deny":["tools:exec_high_risk"]},
"audit":{"trace_id":"...","parent_id":"..."}
}
Policy Hooks:
- Tool allowlist per role & environment (dev/twin/prod)
- Max step depth / timebox per task
- Mandatory evidence: snapshot_id + intent results for any "verify"
MCP (Model Context Protocol)
Tool Manifest (excerpt):
{
"name":"ipfabric-tools",
"tools":[
{"name":"latest_snapshot","input":{},"output":{"snapshot_id":"str","ts":"iso"}},
{"name":"path_lookup","input":{"src":"str","dst":"str"},"output":{"paths":"[]"}},
{"name":"intent_results","input":{"policy":"str"},"output":{"pass":"bool","evidence":"url"}}
],
"memory":{"kv":{"ttl_s":3600},"long_term":true},
"instructions":"Always verify outputs against Intent rules before returning."
}
MCP Advantages:
- One contract to package tools/memory/instructions
- Reusable across LLMs and frameworks
- Easier compliance reviews (static manifest)
4) Reference Loop — Plan • Simulate • Approve • Execute • Reflect
Pseudo‑code: Agent loop with guardrails
function AGENT_RUN(goal):
obs = tools.latest_snapshot() + tools.intent_results("*")
plan = lrm.plan(goal, obs)
sim = twin.whatif(plan.proposed_cfgs)
gate = verify.intents(sim, policies="all") && risk(sim) <= threshold
if not gate: return escalate_with_evidence(plan, sim)
ticket = human_approve(plan, sim, gate)
if not ticket.approved: return "HITL declined"
exec = automation.apply(plan.steps, rollback_on_error=true)
post = verify.intents("*") && compare_slo(pre=obs, post=tools.latest_snapshot())
write_evidence({plan,sim,exec,post}); learn({signals, outcome})
5) Practical Multi‑Agent Playbooks (No code)
Playbook A — Incident War‑Room (A2A)
- DiagnosticsAgent gathers evidence (paths, intents, deltas).
- SecurityAgent checks segmentation/ACL impacts.
- StabilityAgent proposes least‑blast‑radius fix → twin sim → approval.
Playbook B — Compliance Sweep
- ComplianceAgent runs policies across snapshots; tags violations.
- ChangeAgent batches fixes; groups by maintenance windows; verifies in twin.
Playbook C — Capacity & Resilience
- PerformanceAgent forecasts congestion; routes proposals to ChangeAgent.
- RunbookAgent compiles evidence pack for CAB; auto‑generates rollback.
Week 4 Deliverables
- A2A/ACP/MCP specs & sample manifests
- Agent policy allowlists + safety gates
- Plan–Simulate–Approve–Execute–Reflect loop template
- 3 multi‑agent playbooks + KPIs (MTTR, CFR, success rate)