Architecting Enterprise AI Systems

Given a system/product idea, run it through the 7-Layer Analysis Framework and produce a structured architecture breakdown with folder tree, risk map, and roadmap — not a shallow summary.

Trigger conditions: system/agent architecture, AI orchestration, security framework design, product roadmap, monetization strategy, infrastructure planning, multi-domain technical exploration.

Non-trigger conditions: simple facts, casual conversation, generic creative writing, quick summaries, basic formatting/translation. For these, answer directly and briefly — do not apply the full framework.

Progress checklist for triggered requests:

• Step 1: Classify — is this a system/architecture/strategy request, or a simple query? (see triggers above)
• Step 2: Problem Understanding — state the real problem, hidden objective, constraints, dependencies
• Step 3: System Perspective — ecosystem impact, module integration, scalability, maintainability
• Step 4: Innovation Perspective — automation angles, AI orchestration opportunities, new use-cases
• Step 5: Security Perspective — attack surface, abuse cases, zero-trust gaps, failure modes
• Step 6: Business Perspective — monetization model, target market, pricing, adoption path
• Step 7: Technical Perspective — concrete architecture layers, data flow, persistence, concurrency
• Step 8: Execution Perspective — ordered implementation steps, priorities, deployment, observability
• Step 9: Render output as folder tree + layer breakdown + risk map + roadmap (see Output Format)

Example 1 (good — full framework applied):

Input: "We want a module in our AI platform that manages trust scores between autonomous agents, so a compromised agent gets isolated automatically."

Output:

PROBLEM UNDERSTANDING
Real problem: no mechanism exists to detect and contain a misbehaving agent
before it corrupts shared state or is exploited as an attack vector.
Hidden objective: enable safe scaling to 50+ agents without manual babysitting.
Constraint: must not add >50ms latency to inter-agent calls.

SYSTEM PERSPECTIVE
Sits between the message bus and the agent registry. Every inter-agent call
gets scored; low-trust agents get routed through a sandboxed proxy instead
of direct access. Long-term: this becomes the basis for a reputation ledger
usable across agent versions.

ARCHITECTURE
/core/trust-governor/
  scoring-engine.ts       # computes trust delta per agent action
  isolation-handler.ts    # quarantines agent below threshold
  audit-log.ts            # append-only log of score changes + reasons
  policy-config.yaml      # thresholds, decay rate, recovery rules
/core/trust-governor/tests/
  scoring-engine.test.ts
  isolation-handler.test.ts

FLOW
1. Agent A calls Agent B via message bus
2. Trust Governor intercepts call, checks current score of A
3. If score >= threshold: call proceeds directly
4. If score < threshold: call routed through sandboxed proxy with reduced
   permissions, and event logged
5. Every action outcome (success/failure/anomaly) updates A's score via
   scoring-engine.ts (exponential decay + penalty weighting)
6. If score drops below hard floor: isolation-handler.ts revokes bus access,
   fires alert event, requires manual or automated re-certification

SECURITY
- Replay protection: each scored action includes a nonce + timestamp,
  rejected if seen before (prevents score manipulation via replay)
- Privilege escalation: sandboxed proxy runs with capability-based
  permissions, not inherited agent permissions
- Resource exhaustion: rate-limit score-affecting calls per agent per minute

RISK MAP
| Risk                          | Likelihood | Impact | Mitigation                     |
|-------------------------------|-----------|--------|---------------------------------|
| False positive isolation      | Medium    | Medium | Grace period + manual override  |
| Score manipulation via replay | Low       | High   | Nonce + timestamp validation     |
| Governor becomes bottleneck   | Medium    | High   | Async scoring, sync gate check   |

MONETIZATION
Trust-Governor-as-a-Service: enterprise clients running their own agent
fleets pay per-agent-monitored/month. Audit log export as compliance
add-on for regulated industries.

ROADMAP
1. scoring-engine.ts + unit tests (week 1)
2. isolation-handler.ts + sandboxed proxy (week 2)
3. audit-log.ts + append-only storage (week 2)
4. integration with message bus + load test at 50 agents (week 3)
5. alerting + manual re-certification flow (week 4)

Example 2 (bad — what NOT to produce, shown for contrast):

Input: same as above.

Bad output (avoid this):

Great idea! You could add a "trust score" to each agent and check it
before allowing actions. This would make your system more secure and
scalable. You might want to use a database to store the scores and
maybe add some logging too. Let me know if you want more details!

This fails because: no concrete architecture, no file structure, no security analysis, no risk map, generic phrasing ("you might want to"), no implementation order, treats a system design request like small talk.

Example 3 (non-trigger — correctly stays lightweight):

Input: "What's the capital of Indonesia?"

Output: "Jakarta." (No framework applied — this is a simple factual question, not a system/architecture request.)