Design V6 runtime infrastructure around AI agents — permissions, tools, MCP/Skills/Hooks, feedback loops, observability, scheduled routines, von Neumann-style runtime architecture, and governance. Use when deploying agents to production, designing multi-agent systems, building agent harnesses, or turning Obsidian wiki rules into bounded runtime controls.
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Design V6 runtime infrastructure around AI agents — permissions, tools, MCP/Skills/Hooks, feedback loops, observability, scheduled routines, von Neumann-style runtime architecture, and governance. Use when deploying agents to production, designing multi-agent systems, building agent harnesses, or turning Obsidian wiki rules into bounded runtime controls.
version
6.1
updated
2026-07-02
assumes
The agent workflow will use tools, permissions, logs, or production-like reliability boundaries.
conflicts_with
Do not relax approval, sandbox, or observability constraints introduced by agentic-engineering or agent-teams-command.
Harness Engineering
Design the system around AI agents for reliable, safe production use.
Harness Engineering protects the quality ceiling of Agentic Engineering. It turns fast agent output into controlled execution through permissions, observability, recovery, and adversarial validation.
Agent Runtime Model
Treat the agent harness as the kernel around an LLM OS:
Kernel concern
Agent harness responsibility
Memory management
Curate context, summarize bulky outputs, persist state to wiki/logs.
Syscall boundary
Expose tools with contracts, allowlists, deny rules, and retries.
Process isolation
Separate write scopes, sandboxes, credentials, and state per agent.
Scheduling
Decide sequential, parallel, or event-driven execution.
Interrupts
Stop, ask approval, rollback, or route to a safer action.
Observability
Log tool calls, decisions, outputs, costs, and verification evidence.
Garbage collection
Close idle agents, remove stale tasks, compact context, and record risks.
Von Neumann Harness Frame
Use this frame to turn an agent workflow into an inspectable runtime. The value is not the metaphor; the value is forcing every delegated action to name its program, memory, control path, tools, disk, verifier, and recovery boundary.
Runtime part
Harness responsibility
Stored program
Skills, prompts, SOPs, routines, and command boards are executable instructions with versions and owners.
Processor / control unit
One auditable scheduler decides the next action, interrupt, rollback, or escalation.
RAM
Context is hot working memory; load only task-local state and prune stale context.
Disk
Wiki, logs, state files, sessions, traces, and receipts hold durable memory outside chat.
Bus
Tool calls, event streams, task lists, and handoff schemas move state between components.
I/O devices
MCP servers, shell commands, browsers, APIs, filesystems, and external connectors are system calls.
Verifier
Tests, lint, evals, receipts, telemetry, reviewers, or red teams check state transitions.
Garbage collector
Cleanup idle agents, stale tasks, outdated context, obsolete rules, and unreconciled artifacts.
Serial-control rule: keep high-risk intent, permission escalation, and final join decisions on one inspectable control path. Parallelize only owned workstreams whose memory, bus, tools, and verifier are already defined.
Automata rule: local rules create global behavior. For each routine, hook, or agent worker, define state + trigger -> action -> evidence -> next state | stop | escalate.
Self-reproduction rule: an agent may propose new skills, hooks, schemas, or automations, but installation requires the V6 promotion gate, provenance, human approval when required, and a cheap objective check.
Productized Agent Harness
Google I/O '26 added a practical pressure test for harness design: the same runtime pattern now appears in developer tools, personal agents, search, commerce, generative media, and smart glasses.
Product surface
Harness control that must exist
Agent-first IDE
task queue, subagent ownership, hooks, sandbox, test proof
Personal agent
user mandate, memory scope, tool allowlist, resumable log
If a harness cannot produce an audit trail for what the agent saw, decided, called, changed, and verified, the agent is not ready for delegated action.
Managed Agent Runtime Model
For production-like agents, separate the runtime into three resources:
Resource
Defines
Harness questions
Agent
Model, persona, system prompt, skills, MCP/tools
Is the role narrow enough? Are capabilities necessary?
What can the agent reach? What is allowlisted or denied?
Session
Agent instance, mounted context, event stream, durable state
How is state resumed, deleted, audited, and recovered?
The session event log is the backbone of reliability. It should capture user messages, tool calls, tool results, agent responses, verification evidence, errors, and recovery actions. A response without an inspectable event trail is not enough for delegated production work.
Permission Bike Method
Escalate autonomy by proven reliability, not by confidence in a prompt:
Stage
Allowed capability
Required proof
Observe
Read, search, summarize, recommend
Sources and assumptions are inspectable.
Co-drive
Draft, simulate, prepare changes
Human approves every external action.
Training wheels
Execute low-risk scoped actions
Logs, rollback, and post-action checks pass.
Supervised autonomy
Run reversible routines
Alerts, receipts, and anomaly review exist.
Autonomy
Run high-frequency low-risk loops
Periodic permission audit and failure review.
Do not give a tool key and rely on text instructions to prevent misuse. The real boundary is the key, endpoint, account, filesystem, network, budget, and approval path.
Usage Template
Prompt
Use harness-engineering for this agent workflow. Design permissions, tools, feedback loops, observability, and failure handling.
Use Case
Moving an agent workflow from ad hoc prompting toward a reliable runtime architecture.
Expected Result
The agent produces a harness design with permission tiers, tool boundaries, logs, evals, and recovery paths.
Output Example
A runtime spec with permission matrix, tool allowlist, approval gates, logs, evals, and incident response.
Verification Case
The design names what the agent can do automatically, what needs approval, and what is denied.
Verified Effect
An ad hoc agent workflow becomes a controlled runtime with explicit permissions, observability, and failure handling.
Before any tool call that buys, publishes, schedules, messages, edits shared state, or acts through a user's account, require:
Mandate: what the user explicitly authorized
Scope: allowed accounts, surfaces, vendors, files, or domains
Limit: budget, time, rate, data, or blast radius cap
Preview: what the user can inspect before execution
Receipt: durable audit record after execution
Rollback: how to undo or compensate if wrong
3. Architectural Constraints
Linters, structural tests
File/directory access boundaries
Token budget: never exceed 80% context window
4. Feedback & Validation Loops
Write-Test-Fix cycle
Generator + Evaluator pattern (GAN-inspired)
Deny-and-continue: try safer alternative on block
Escalate to human after 3 consecutive blocks
For high-risk work: Builder -> Evaluator -> Red Team -> Fixer -> final proof
5. Observability & Governance
Log every tool call + result
Metrics: success rate, revert rate, token usage
Cost tracking per session
Evidence ledger: verification command, source link, screenshot, test result, or diff for each completion claim
Provenance ledger for generated media, search summaries, and commerce decisions: source, prompt/edit trail, model/tool used, and user confirmation point
6. Maintenance
Periodic agents: dead code, outdated docs, architectural drift
Wiki lint for broken links/missing frontmatter
Context garbage collection: summarize completed work, close idle processes, and record residual risk
GAN-Inspired Multi-Agent Pattern
"Tuning a standalone evaluator to be skeptical is far more tractable than making a generator critical of its own work."
Which paths, domains, records, or resources are allowed?
Failure mode
What is the safe fallback when it fails or is denied?
Evidence
What output proves it succeeded?
Audit
Where is the call logged?
Prefer narrow tools with explicit inputs over broad shell/API access. If broad access is unavoidable, wrap it with approval gates and post-action verification.
V6 Extension Primitive Selector
Choose the lowest-context primitive that can do the job:
Primitive
Use for
Avoid when
Skill
Reusable procedure, local convention, task policy, source-to-output workflow
Deterministic check can run outside context
Hook
LSP/lint/test/secret scan, tool-call guard, CI or PR signal, post-action receipt
The result needs human judgment or broad context
MCP / Connector
Public or cross-product integration, external API, authenticated system access
A local file/script or narrow hook is enough
Dynamic workflow
Many independent shards with script review, cost cap, and observable workers
Roles need frequent IPC or shared judgment
Agent team
Distinct roles, ownership, critique, integration, or debate
One thin loop plus test can solve it
Default to zero-overhead context: do not inject a tool, rule, report, or map into the prompt unless the current task uses it.
V6 Trigger-Context-Steering Harness
For scheduled routines and proactive agents, define:
Trigger: schedule, event, webhook, human request, or queue threshold
Context: exact files, tools, credentials, memory, and wiki pages visible to the agent
Steering: live observation, approval gate, verifier, rollback, anomaly alert
Receipt: durable log, diff, dashboard, daily note, or external system status
No routine should run indefinitely. Every routine needs a hard budget, stop condition, stale-context half-life review, and recovery path.
Security-Aware Integration
Before shipping high-risk agent output, run an adversarial review:
Risk area
Minimum adversarial check
Auth, permissions, secrets
Attempt privilege escalation and secret exposure paths.
Data mutation or deletion
Verify backups, rollback, idempotency, and explicit target bounds.
Public claims or launch copy
Check evidence, source quality, and unsupported promises.
External API actions
Confirm rate limits, credentials, audit logs, and retry safety.
Commerce or delegated account actions
Verify mandate, budget, merchant/payment boundary, receipt trail, and rollback path.
Generated media or public content
Verify provenance, disclosure/watermark path, and unsupported claim risk.
Multi-agent integration
Check ownership conflicts, stale assumptions, and unverified joins.
Use simulated hostile agents, security tests, or a skeptical evaluator before release. Do not let the same builder be the only judge of safety.
Closed-Loop System (Aladdin)
Data Lake → Risk Engine → Optimizer → Stress Test → OMS → Compliance → Feedback Loop
↑ ↓
└──────────────────── 反馈闭环 ────────────────────────────────┘
Aladdin Module
Agent Harness
Data Lake
Session log, knowledge base
Risk Engine
Risk assessment, threat detection
Optimizer
Task decomposition, resource allocation
Stress Test
Edge-case testing, failure simulation
OMS
Order execution, tool calls
Compliance
Guardrails, policy enforcement
Feedback Loop
Results → learning → improvement
Key: Closed loop is the moat — not any single component. MECE principle applies.
Quality Gates
Each agent has defined permission scope
Agent, Environment, and Session responsibilities are separated
