Architecture

Reference Architectures

Production-grade architecture blueprints for enterprise AI systems — context-engineered, self-healing, and built for scale.

Docker Multi-Framework AI Agent Orchestration Platform

3 Frameworks • 12+ Autonomous Agents • Containerized • CIS-Hardened
Production-grade Docker orchestration unifying Claude Code, LangGraph, and Microsoft Agent Framework
Claude Code Agents LangGraph Deep Agents Microsoft Agent Framework CIS Docker Benchmark Zero-Trust Containers
📐 Conforms to: Daly Framework (2026) 📄 Version 1.0 📅 February 2026 🔗 GitHub
Architecture Non-Negotiables
1
Framework Sovereignty
Each AI framework runs in isolated containers with independent runtimes. No shared process space. Claude Code, LangGraph, and Microsoft agents never co-mingle state.
2
CIS-Hardened by Default
Every container: cap_drop ALL, no-new-privileges, read-only rootfs, pids_limit 256, tini PID 1. No exceptions. Security is structural, not optional.
3
Observable Communication
All inter-agent communication flows through inspectable channels: JSON files on shared volumes, LangGraph state checkpoints, or gRPC streams. No hidden side-channels.
Unified Orchestration Topology
Docker Compose — Unified Control Plane
Single docker-compose.yml with YAML anchors (x-agent-defaults) • Framework-specific overlays • Shared agent-net bridge network
🏗
Compose Orchestrator
YAML anchors eliminate duplication • x-agent-defaults, x-agent-environment, x-agent-volumes
Framework overlays: docker-compose.langgraph.yml, docker-compose.msagent.yml
Combined: docker compose -f ... -f ... -f ... up --build
🌐
agent-net Bridge
Isolated bridge network
Docker DNS resolution
Outbound: API endpoints only
🔄
Init Service
Alpine container pre-creates
volume directory structure
Agents wait via depends_on
▼ ▼ ▼
Docker Compose orchestrates all three framework stacks in parallel
Framework 1 — Claude Code Agents (Master-Controller Pattern)
Claude Code — 6 Autonomous Agents
Single base image (Dockerfile.base) • 4-line thin Dockerfiles per agent • File-based IPC via shared Docker volumes • JSON protocol contracts
🧠
Master Controller
Decomposes incoming tasks • Delegates to 5 specialists
Monitors progress via polling (15s) • Aggregates final results
Reads: /app/workspace/.tasks/incoming.json
🔍
Researcher
Codebase analysis
Architecture mapping
Technical findings
💻
Coder
Feature implementation
Bug fixes • Refactoring
Code generation
🔎
Reviewer
Code review • OWASP audit
Best-practice enforcement
Tester
Test authoring • Execution
Coverage • Regression detection
🚀
Deployer
Dockerfiles • CI/CD pipelines
Deployment scripts • Infra config
📁 IPC: shared-tasks/ • shared-status/ • shared-output/ (Docker named volumes) 📜 Contracts: task.schema.json • status.schema.json • output.schema.json
▼ ▲   ▼ ▲   ▼ ▲
Read / Write ↔ Shared Docker Volumes (JSON Protocol)
Framework 2 — LangGraph Deep Agents (Graph-Based Orchestration)
LangGraph — Stateful Graph Execution
langgraph build generates Docker images • Graph-based workflows: nodes, edges, conditional routing • State checkpointing to PostgreSQL
🗺
LangGraph API Server
Graph execution runtime • REST + streaming endpoints
Manages agent state machines • Human-in-the-loop breakpoints
Multi-agent topologies: supervisor, handoff, hierarchical teams
🧭
Planning Agent
Deep Agents SDK • deepagents CLI
Task decomposition
Sub-agent spawning
Tool Executor Nodes
Filesystem access
Code execution
API integration tools
💾
PostgreSQL (Checkpoint)
State persistence • Graph checkpoints
Replay • Time-travel debugging
pgvector for embeddings
Redis (Pub/Sub)
Real-time state streaming
Cross-agent notifications
Task queue coordination
🔄
LangSmith Tracing
Observability • Run traces
Token tracking • Latency
Evaluation datasets
🖧 Topologies: Sequential • Fan-Out/Fan-In • Supervisor • Handoff • Hierarchical Teams 🔄 State: Checkpoint → Resume • Branch • Replay • Human-in-the-loop
LangGraph Agent Execution Loop:
1
INVOKE
2
ROUTE
3
EXECUTE
4
CHECKPOINT
5
EVALUATE
6
LOOP / END
▼ ▼ ▼
LangGraph state checkpoints persist to PostgreSQL • Events stream via Redis
Framework 3 — Microsoft Agent Framework (Semantic Kernel + AutoGen)
Microsoft Agent Framework — Unified SDK
Semantic Kernel (v1.39+) + AutoGen (v0.4) merged Oct 2025 • Graph-based workflows • MCP/A2A protocol support • gRPC distributed runtime
🧩
Semantic Kernel Runtime
Kernel + Plugins + Planners + Memory + Agents
Native functions + OpenAPI plugins + MCP tools
Multi-model: Azure OpenAI, Anthropic, Ollama, HuggingFace
🤖
AutoGen Agents
Actor model runtime
Multi-agent conversations
Group chat patterns
💬
Agent Chat Protocol
Structured messaging
Tool call routing
Conversation history
🖧
gRPC Distributed Runtime
Cross-container agent comm
Protobuf serialization
Service mesh ready
📦
Docker Code Executor
Sandboxed code execution
Per-task containers
Filesystem isolation
🧠
Kernel Memory
RAG pipeline container
Document ingestion
Semantic search • Vector store
🔗 Protocols: MCP (Model Context Protocol) • A2A (Agent-to-Agent) • gRPC • OpenAPI ☁ Azure AI Foundry integration • Local-first with Docker • Cloud-optional
▼ ▲   ▼ ▲   ▼ ▲
gRPC distributed runtime ↔ Kernel Memory ↔ Docker Code Executor
Container Security — CIS Docker Benchmark Compliance
Security Hardening Layer
Every container in every framework stack enforces these controls. No exceptions. Verified by CI on every commit.
🛡
cap_drop: [ALL]
CIS 5.3 • All Linux capabilities dropped
No privilege escalation surface
PostgreSQL gets targeted cap_add
🔒
no-new-privileges
CIS 5.25 • security_opt enforced
Prevents setuid/setgid escalation
Applied via x-agent-defaults anchor
💾
read_only: true
CIS 5.12 • Immutable root filesystem
Writable: tmpfs (/tmp, ~/.cache)
+ explicit volume mounts only
🚫
pids_limit: 256
CIS 5.28 • Fork bomb prevention
Resource exhaustion guard
Per-container enforcement
tini PID 1
CIS 5.29 • Proper signal handling
Zombie process reaping
Clean SIGTERM propagation
👤
USER node (non-root)
CIS 5.15 • All agents run as node user
No root access in any container
Least-privilege enforcement
📊
Resource Limits
CIS 5.10/5.11 • CPU: 2 cores max
Memory: 4 GB max • 512 MB reserved
deploy.resources.limits enforced
📝
Log Rotation
CIS 5.7 • json-file driver
max-size: 10m • max-file: 5
Prevents disk exhaustion
🔍
Trivy Scanning
CI pipeline vulnerability scan
Base image + dependencies
Block on CRITICAL/HIGH CVEs
🔐
Secret Management
.env file (dev) • Docker secrets (prod)
Never in Dockerfiles or CLI args
.claude/ mounted read-only
Communication & Data Architecture
Inter-Agent Communication
Claude Agents — File-Based IPC
JSON files on 3 shared Docker volumes: tasks/, status/, output/. Per-agent subdirectories. Atomic writes via tmp+mv. Polling-based (15s).
LangGraph — State Graph + Checkpoints
Graph state persisted to PostgreSQL. Real-time events via Redis pub/sub. Checkpoint → resume → branch → replay.
Microsoft — gRPC + Actor Model
Protobuf-serialized messages over gRPC. AutoGen actor runtime for multi-agent conversations. Docker DNS service discovery.
Cross-Framework — Shared Workspace
All frameworks mount ./workspace at /app/workspace. Common codebase access. Framework outputs readable by others.
Data & State Persistence
📁 Docker Named Volumes
shared-tasks/ • shared-status/ • shared-output/
Persist across container restarts
Explicit cleanup: make clean
💾 PostgreSQL
LangGraph state checkpoints • pgvector embeddings
Microsoft Kernel Memory store
Healthcheck: pg_isready
⚡ Redis
LangGraph real-time streaming
Task queue coordination
Pub/sub event bus
📜 JSON Schema Contracts
task.schema.json • status.schema.json
output.schema.json • Validated in CI
Single source of truth for IPC
Container Image Strategy
Image Hierarchy
📦
claude-agent-base (Dockerfile.base)
FROM node:22-slim • Single source of truth for all Claude agents
Installs: Node.js, Claude Code CLI, tini, git, jq, curl
OCI labels: BUILD_DATE, VCS_REF • Healthcheck: pgrep -x node
🔧
6 Agent Images
4-line thin Dockerfiles each
FROM + LABEL + COPY + ENV
Zero duplication
🗺
LangGraph Image
langgraph build generates image
Python runtime + dependencies
Graph definitions baked in
🧩
Microsoft Agent Image
.NET / Python base
Semantic Kernel + AutoGen
gRPC runtime included
💾
Infrastructure Images
postgres:16-alpine (checkpoints)
redis:7-alpine (pub/sub)
Official images, pinned tags
CI/CD — GitHub Actions Pipeline
Continuous Integration & Deployment
GitHub Actions on push/PR to main • Structure validation + JSON schema checks + Trivy scan + multi-arch build
Structure Check
Verify all agent dirs exist
Required files: Dockerfile,
system-prompt.md, CLAUDE.md
📜
Schema Validation
jq validates all JSON files
schemas/ + examples/ + mcp-config
Fail counter: exits non-zero
🛡
Trivy Scan
aquasecurity/trivy-action
Scans claude-agent-base image
Blocks on CRITICAL severity
🚀
Build & Tag
docker build with BUILD_DATE
VCS_REF from git rev-parse
Semantic versioning (v1.0.0+)
Deployment Modes
1
Dev
make chat
Single interactive agent
Ad-hoc development
2
Pair
make cowork
Lead + Reviewer
Pair programming mode
3
Team
make team
Full 6-agent Claude stack
Complex task decomposition
4
Multi-FW
make platform
All 3 frameworks
12+ agents orchestrated
5
MCP
make mcp
+ GitHub, Search, DB
Full tool integration
⚡ Quick start: 3 commands to fully operational — make setup → make build → make team
🛡 Production path: Pin CLAUDE_CODE_VERSION • Use Docker secrets • Enable Trivy in CI • Add network policies
Scale targets: Single Docker host (default) → Docker Swarm (multi-host) → Kubernetes (enterprise)
MCP Server Integration (Tool Servers)
Model Context Protocol Overlay
docker-compose.mcp.yml overlay • Adds tool servers that all agent frameworks can access via Docker DNS
💻
mcp-github
Repos, Issues, PRs
Requires: GITHUB_TOKEN
Healthcheck: HTTP probe
📁
mcp-filesystem
Structured file I/O
Scoped to /app/workspace
No external dependencies
🔍
mcp-brave-search
Web search capability
Requires: BRAVE_API_KEY
Rate-limited queries
💾
mcp-postgres
Database access
Shared with LangGraph
SQL query execution
MIT License

Copyright © 2026 AlphaOne LLC

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Enterprise AI Agent Reference Architecture

100 AI Agents • 10 Deployment Groups (A1–A10) • 10 Agents per Group
Context-engineered architecture with autonomous health monitoring and self-healing design
Azure Native GPT-5.2 Responses API Self-Healing Autonomous Health Zero-Trust Isolation
📐 Conforms to: Daly Framework (2026) 📄 Version 3.0 📅 February 2026
The Three Non-Negotiables
1
Structural Isolation
Data-plane enforced. tenant_id as mandatory predicate BEFORE ranking. IsolationBreach exception on mismatch. Never delegated to the model.
2
Deterministic Replay
Every run captures model version, policy version, prefix hash, artifact IDs, tool contracts, token counts. Trace envelopes are the unit of evaluation.
3
Economic Predictability
4 cost surfaces per run: Inference + Retrieval + Tooling + Persistence. Layer-based token budgets. Progressive disclosure over context dumps.
External Entry Point
ChatGPT Integration
💬
Custom GPT (ChatGPT)
OpenAPI Actions • OAuth 2.0 + PKCE • Entra ID callback
User invokes action → Entra ID auth → Bearer token → APIM
🔒
OAuth 2.0 Flow
Auth Code + PKCE
MFA + Conditional Access
SSO via Entra ID
🌐
API Endpoints
/api/v1/query • /api/v1/agents/{group}/{id}
/api/v1/health • /api/v1/traces/{runId}
▼ ▼ ▼
HTTPS + Bearer Token (JWT)
API Gateway & Identity
Gateway & Identity Layer
🛡
Azure API Management
Rate limiting • JWT validation • IdentityEnvelope construction
OpenAI API proxy • Token governance • Content Safety scan
👤
Microsoft Entra ID
OAuth 2.0 provider • RBAC roles
Conditional Access • Managed Identities
🔑
IdentityEnvelope
tenant_id (tid) • user_id (oid)
roles • privacy_mode • policy_version
▼ ▼ ▼
IdentityEnvelope + Scoped Request → Context Engine Loop
Context Engine Loop (10-Step — Every Agent, Every Run)
Context Engine Loop
1
INGEST
2
PLAN
3
RETRIEVE
4
ASSEMBLE
5
STABILIZE
6
GC
7
INFER
8
PROMOTE
9
TRACE
10
LIFECYCLE
Layer-Based Token Budget (4,000 tokens total):
Global 500
Tenant 800
User 600
Retrieved 1,200
Session 900
▼ ▼ ▼
Orchestrator dispatches to Agent Groups
100 AI Agents — 10 Deployment Groups (A1–A10)
Compute: Agent Groups (Generic Deployment Units)
Each group: dedicated Container Apps Env • dedicated Managed Identity • independent scaling • blue-green deployable • function assigned post-deploy
A1
10 Agents
AGT-001–010
cae-agents-a1
A2
10 Agents
AGT-011–020
cae-agents-a2
A3
10 Agents
AGT-021–030
cae-agents-a3
A4
10 Agents
AGT-031–040
cae-agents-a4
A5
10 Agents
AGT-041–050
cae-agents-a5
A6
10 Agents
AGT-051–060
cae-agents-a6
A7
10 Agents
AGT-061–070
cae-agents-a7
A8
10 Agents
AGT-071–080
cae-agents-a8
A9
10 Agents
AGT-081–090
cae-agents-a9
A10
10 Agents
AGT-091–100
cae-agents-a10
🔄 Patterns: Sequential • Concurrent Fan-Out • Handoff • Group Chat ⚙ Scoped Delegation • Isolated Context • Typed Summaries • parent_run_id Lineage
▼ ▲   ▼ ▲   ▼ ▲
Read / Write ↔ Canonical Truth + Derived Acceleration
Data Architecture: Truth vs Acceleration
Canonical Truth (Source of Record)
💾 Cosmos DB: Canonical Event Log
Container: audit-log • Partition: /runId • Append-only
Every run: context, policies, tool calls, promotions, outputs
Replicated to Blob Storage with legal hold
📊 Cosmos DB: Structured Memory
Container: structured-memory • Partition: /tenantId
Scoped • Typed • Provenance • Retention • Sensitivity
States: provisional → active | quarantined | revoked
⚙ Cosmos DB: Agent Config
Container: agent-config • Partition: /groupId
Runtime config for AGT-001 through AGT-100
📁 Azure Blob Storage
objects/ — SHA-256 content-addressed, tenant-scoped
legal-hold/ — Immutable event log replica
Derived Acceleration (Rebuildable)
🔍 Azure AI Search (Hybrid)
DiskANN vector + BM25 lexical • Fully rebuildable
Mandatory predicates: tenant_id, scope, expiration
Filters BEFORE ranking — never post-filter
⚡ Hardening Pipeline
Container Apps scale-to-zero workers
Validate → Enrich → Embed → Update indexes
provisional → active (or quarantined)
📈 OpenAI GPT-5.2 API
Responses API via APIM proxy
Thinking: reasoning medium/high • No-reasoning: effort=none
text-embedding-3-large (3072 dims)
💰 Prompt Cache
Static prefix: constitution + tenant policy
prefix_hash = SHA-256 • 24h cache, 90% discount
Memory Architecture: Scoped & Typed
Memory Scopes (Security Boundaries)
GLOBAL — Immutable, Write-Locked
Safety rules, tool contracts, constitution • Versioned artifact bundles
TENANT — Gated Promotion (Human Approval)
Org policies, playbooks, knowledge bases • Policy-based retention
USER — Gated Promotion, TTL + User Controls
Preferences, working style, notes • User-deletable
SESSION — Volatile, Aggressive Auto-GC
Tool outputs, scratch buffers • Hours–days TTL • Never overrides durable memory
Memory Types (Semantic Roles)
POLICY
Normative rules • Versioned, signed, NEVER agent-writable
PREFERENCE
Stable personalization • TTL-based, user-deletable
FACT
Durable assertions • Must include provenance and source
EPISODIC
Structured summaries • What happened, NOT what to always do
TRACE
Raw append-only execution events • Immutable flight recorder
⚠ DANGER: Episodic/Fact drifting into Policy = Precedent Poisoning. Promotion gates enforce separation.
Security, Encryption & Network
Security & Encryption Layer
🔐
Azure Key Vault
Per-tenant KEKs • Rotating DEKs
OpenAI API key • Cert mgmt
🛡
Content Safety
Prompt Shields • PII Detection
Jailbreak scanning • Task adherence
🌐
VNet Isolation
snet-apim (10.0.1.0/24)
snet-compute (10.0.2.0/24)
snet-pe (10.0.4.0/24) • Private endpoints
🔏
Envelope Encryption
Canonical: tenant KEK+DEK
Object Store: tenant-scoped
Cache: short-lived keys
🚫
Isolation Enforcement
tenant_id mandatory predicate
IsolationBreach exception
Privacy routing (no-retention)
AI Autonomous Health Monitoring System
Health Sentinel (Meta-Agent — Monitors All 100 Agents)
Dedicated hardened partition • Outside A1–A10 groups • Elevated RBAC • Autonomous operation
📡 Health Collector
30s polling • /healthz + /readyz
OpenTelemetry metrics • Trace stats
Container Apps platform metrics
🧠 Diagnostics Engine
GPT-5.2 reasoning=high
Root-cause analysis
Cross-group correlation
🔧 Remediation Orchestrator
12 playbooks (PB-001–012)
Restart, scale, rotate, rebuild
Blast radius scoped
📝 GitHub Integration Agent
Auto-create Issues • Full lifecycle mgmt • Labels: severity, group, type, status
Trace envelope links • Reopen on recurrence • Monthly reports
📊 Governance Reporter
Periodic health reports • SLA adherence • Compliance dashboards
MTTD/MTTR • Playbook effectiveness • Cost impact
5-Level Health Data Collection:
L1
Infrastructure
CPU/Memory • Restarts
Network latency
Cosmos RU • Search
L2
Agent Runtime
Request rate
p50/p95/p99
Loop step durations
L3
Context Health
Budget utilization
Retrieval hit rates
Promotion ratios
L4
Cost Health
Per-run breakdown
Token drift trends
Cost per group
L5
Security Health
IsolationBreach=0
Auth failures
Key rotation
Health Score (0–100, computed every 60s per agent):
90–100: HEALTHY 70–89: DEGRADED 50–69: UNHEALTHY (auto-remediate) 0–49: CRITICAL (immediate + escalate)
AI Self-Healing Architecture
Closed-Loop Self-Healing Control
🔍
DETECT
Score < threshold
🧠
DIAGNOSE
GPT-5.2 RCA
📋
PLAN
Select playbook
🔧
REMEDIATE
Execute actions
VERIFY
Re-check health
📝
RECORD
GitHub Issue
💡
LEARN
Episodic memory
12 Automated Remediation Playbooks:
PB-001
Container Restart
PB-002
Scale Out
PB-003
Budget Rebalance
PB-004
Isolation Emergency
PB-005
Promotion Throttle
PB-006
Pipeline Scale
PB-007
Config Sync
PB-008
RU Scale
PB-009
Index Rebuild
PB-010
Key Rotation
PB-011
Resource Increase
PB-012
Cost Investigation
Escalation Matrix:
SEV-1: CriticalIsolation breach, data exposure • Immediate page + quarantine
SEV-2: HighMulti-agent down • Auto-remediate, escalate @ 3 fails
SEV-3: MediumSingle agent degraded • Auto-remediate + tracking Issue
SEV-4: LowMinor drift, config warning • Log + GitHub Issue
GitHub Issues Integration & Remediation Tracking
GitHub Ops (enterprise-ai-agents-ops)
Issue Labels:
severity/p1-critical severity/p2-high severity/p3-medium severity/p4-low
type/auto-remediation type/security-incident type/cost-anomaly type/isolation-breach
status/detecting status/diagnosing status/remediating status/resolved
group/a1 group/a2 ... group/a10
Issue Lifecycle:
1. OPEN — Anomaly detected, labels applied
2. DIAGNOSING — Root-cause analysis appended
3. REMEDIATING — Playbook + action progress
4. VERIFICATION — Post-remediation health check
5. RESOLVED — Closed with outcome summary
6. REOPENED — Recurrence within 24h
7. ESCALATED — Auto-remediation failed
Milestones: Weekly Health • Monthly Governance • Quarterly Security
Observability & Trace Envelopes
Observability Layer
📊
Azure Monitor + App Insights
OpenTelemetry distributed tracing
50 GB/day • KQL analysis
📨
Trace Envelopes
identity • model_ver • policy_ver • prefix_hash
artifact_IDs • promotions • tokens • cost • lineage
Event Hub
Real-time alerting
Hardening pipeline trigger
Health event streaming
💰
4 Cost Surfaces
C = Inference + Retrieval
+ Tooling + Persistence
Per-run via traces
Azure DevOps — CI/CD Orchestrated Deployment
Deployment Pipeline Architecture
All infrastructure is IaC (Bicep modules) • Azure DevOps multi-stage YAML pipelines • Parallel jobs deploy all 10 groups simultaneously
🚀
Pipeline: infra-foundation
VNet, Key Vault, Cosmos DB, AI Search
Event Hub, APIM, Entra App Regs
~25 min (single run)
Pipeline: agent-groups-deploy
10 parallel stages (A1–A10 simultaneous)
Each: Container App Env + 10 agent revisions
~12 min (all 100 agents)
🧠
Pipeline: platform-services
Health Sentinel + Self-Healing + GitHub Ops
Context Engine + Hardening Pipeline
~15 min (parallel with agents)
🔍
Pipeline: validate-and-promote
Smoke tests • Health checks • Isolation tests
Security scan • Blue-green traffic shift
~20 min (gate before GA)
D0
Hour 0–1
IaC Foundation
VNet + Cosmos + KV
APIM + Entra ID
D0
Hour 1–2
All 100 Agents
A1–A10 parallel deploy
Health Sentinel online
D0
Hour 2–3
Validation Gate
Smoke + isolation tests
Self-healing verified
D1–D3
Days 1–3
Shadow Mode
Live traffic mirrored
Chaos + load testing
GA
Day 3–5
Blue-green cutover
100 agents live
Hypercare monitoring
⚡ Total deployment: ~3 hours to fully provisioned (infra + 100 agents + health sentinel + self-healing + ChatGPT integration)
🛡 Total to GA: ~5 days (includes shadow mode validation, chaos testing, security scan, and blue-green cutover)
Pipeline spec: Azure DevOps multi-stage YAML • 10 parallel agent-group stages • Bicep what-if + deployment • Post-deploy gates (health score ≥ 90 required) • Automated rollback on gate failure
MIT License

Copyright © 2026 AlphaOne LLC

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Federal Autonomous Systems — Tiered LLM Architecture

Hybrid on-premises / federated cloud AI architecture for mission-critical autonomous systems.
Optimized for zero latency, dedicated throughput, and operational resilience.
On-Premises First Llama 4 Maverick Zero Latency FedRAMP IL5/IL6 Go-to-Hell Resilience
📐 Conforms to: Daly Framework (2026) 📄 Version 1.0 📅 February 2026
AI Workload Distribution
80–90%ON-PREM
10–20%CLOUD
On-premises server rack/cluster — zero latency, dedicated
FedRAMP IL5/IL6 cloud — top-shelf frontier models
Architecture Tiers
Tier 1 — On-Premises Frontier LLM
Primary Workhorse • 80–90% of AI Workload
ZERO LATENCY
Deploy META Llama 4 Maverick — the production top-shelf model (400B total params, 17B active per token, 128 MoE experts, 1M context) — on dedicated hardware within the server rack/cluster. All 128 experts must stay resident in VRAM for routing despite only 17B activating per token. Handles the bulk of all AI inference with zero API latency, zero contention from other projects, and 100% dedicated throughput. The only USA-made open-source frontier AI certifiable for federal military and intelligence use. Free for commercial use — cost is hardware only.
ModelLlama 4 Maverick (400B / 17B active)
ArchMoE — 128 experts, 1M context
VRAM~400GB FP8 / ~800GB BF16
Hardware$500K – $2M all-in (see table)
LicenseOpen source — free commercial use
LatencyNear-zero (local inference)
Throughput30K–40K+ tokens/sec (optimized)
Isolation100% dedicated to mission
CertUS-origin, certifiable for DoD/IC
Llama 4 Maverick 400B MoE Open Source On-Prem
▼ ▼ ▼
Escalation — Complex Tasks
Tier 2 — FedRAMP Cloud (IL5/IL6)
Top-Shelf Escalation • 10–20% of AI Workload
FRONTIER MODELS
For the subset of tasks requiring absolute top-shelf reasoning, escalate to frontier proprietary models hosted within FedRAMP Impact Level 5 and Level 6 cloud environments. Models allegedly available from Unclass through TS, though real-world latency, token budgets, and API contention from other government projects remain open questions.
CloudFedRAMP IL5 / IL6
ClassUnclass → Secret → TS
LatencyAPI-dependent (variable)
RiskContention from other projects
Claude Opus 4.6 Claude Sonnet 4.6 GPT 5.2 GPT Codex 5.3 xAI 4.1 Gemini 3.1
▼ ▼ ▼
Degraded Mode — Cloud Unavailable
Tier 3 — Solo Sustain (Go-to-Hell)
Contingency • Llama 4 stands alone
RESILIENCE TEST
Critical design question: if cloud APIs go dark — cut off, saturated, or denied — can the on-premises Llama 4 deployment solo sustain 100% of mission AI requirements? This is the “go to hell” scenario. The architecture must be validated against this contingency. If Llama can’t solo sustain, the mission has a single point of failure in cloud connectivity.
TriggerCloud denial / API saturation
ModeOn-prem only, 100% workload
StatusRequires validation testing
QuestionCan Llama 4 solo sustain mission?
Llama 4 — Solo Mode Mission Critical
Classification Level Coverage (Alleged)
UNCLASSIFIED
SECRET
TOP SECRET
All frontier models allegedly available across classification levels per paperwork — real-world latency, token budgets, and API priority TBD.
Key Design Advantages
Zero Latency
On-prem inference eliminates API round-trip. No network dependency for 80–90% of AI workload.
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Dedicated Throughput
No contention from other government projects. 100% of compute allocated to mission.
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US-Origin Open Source
META Llama is the only US-made open-source frontier AI certifiable for DoD and IC use.
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Cost Efficiency
$1–2M hardware is trivial for federal budgets. Zero licensing fees. Massive ROI vs. cloud-only.
Hardware Configuration Options — Llama 4 Maverick (400B MoE)
Configuration Hardware Cost VRAM Throughput Notes
1× DGX H200
(8× H200 GPUs)
~$400–500K 1,128 GB ~30K tok/s Maverick FP8 fits on single node. Solid baseline.
1× DGX B200
(8× B200 Blackwell GPUs)Recommended
~$515K 1,536 GB ~40K+ tok/s Maximum single-node. FP4/FP8 via 2nd-gen Transformer Engine. 3.4× faster than H200.
2× DGX H200
(16× H200 GPUs + InfiniBand)
~$800K–1M 2,256 GB ~30K tok/s Full BF16 precision. Max context. Larger batch sizes.
1× DGX B200 + 1× DGX H200
(Hybrid dual-node)
~$900K–1M 2,664 GB ~40K+ tok/s Maverick primary on B200 + Scout fallback on H200. Redundancy.
All-in
(dual B200 + networking + storage + cooling)
~$1.5–2M 3,072 GB 40K+ tok/s Full production rack. Redundancy, storage, InfiniBand, cooling, integration.
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GPU pricing (Feb 2026): Individual H200 ~$30–40K/chip, DGX H200 (8-GPU) ~$400–500K. B200 Blackwell ~$30–40K/chip, DGX B200 (8-GPU) ~$515K. NVIDIA TensorRT-LLM delivers 40K+ tokens/sec on Blackwell with optimized FP8 Maverick — 3.4× faster throughput and 2.6× better cost-per-token vs H200. Model software is 100% free under Llama 4 Community License.
BOTTOM LINE
That’s still pocket change on a federal contract — and you get zero API latency, zero contention, 100% dedicated, with the only US-origin open-source frontier model certifiable for DoD/IC work. No licensing fees. No shared cloud. No dependency on commercial API availability. The entire AI capability owned and operated within the mission perimeter.
⚠ Open Design Question

Who gets API precedence in the federal cloud? When multiple government projects compete for the same frontier model endpoints, which missions get priority? What are the actual token budgets? Are top-shelf models being degraded by lesser back-office AI projects consuming shared capacity? These unknowns drive the architectural bias toward on-premises first.

MIT License

Copyright © 2026 AlphaOne LLC

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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