How the
Decision Factory Works
Agents become first-class citizens with identity, authorization, and full traceability. Your knowledge workers work with an agent mesh—not spreadsheets and emails—to deliver better decisions, faster decisions, more decisions.
Identity & Auth
Observability
Adapting
Auditable
Signal. Policy. Orchestrate. Optimize.
Every decision becomes data. Data refines policy. Better policy yields better decisions. The cycle compounds: more decisions, better decisions, faster decisions—building a better business every day.
Data
Inventory, lead times, demand, pricing, quality metrics—plus past decisions as new data points
Logic & Strategy
Policies, playbooks, tribal knowledge—readable, writable, improvable by the system
Execution
Generate POs, update schedules, trigger workflows—and log outcomes as new data
Real-Time Intelligence Overlay
Traditional mathematical models need weeks or months to "average in" new information through smoothing. LLMs can overlay real-time intelligence immediately.
A supplier announces a facility closure? That's in your logic layer in seconds—not after months of smoothed historical data finally catches the signal.
The compound effect: Every decision improves the logic layer a little bit. Over thousands of decisions, your decision factory becomes irreplaceable institutional knowledge.
A Self-Organizing Agent Mesh
Add an agent—it's auto-discovered, goals aligned, and immediately starts communicating with the mesh. Every agent you add creates a synergistic effect. Teams form around outcomes.
Add Agent
Deploy a new agent with its capabilities and objectives
Auto-Discover
Mesh detects new agent, maps capabilities to existing needs
Goals Align
Agent goals cascade from org hierarchy, conflicts resolved
Start Communicating
Joins secure channels, shares outputs with related agents
The Network Effect
Whatever a new agent does gets funneled to related agents pursuing aligned goals. Teams form organically around outcomes— each agent's output helps others, and every agent you add amplifies the entire mesh. Not 1+1=2. More like 1+1=3.
Factory Capabilities
The infrastructure that makes the decision factory work—orchestration, coordination, identity, observability, and more.
Orchestration
- APEX Orchestrator
Advanced multi-agent coordination and task routing
- Goal Hierarchy
Org → Team → Agent goal alignment and conflict resolution
- Dynamic Routing
Decisions routed by value, confidence, and capability
Agentic Identity
- Cryptographic Identity
Every agent has unique, verifiable, unforgeable ID
- Capability Declarations
Agents register what they can do, mesh routes accordingly
- Verifiable Credentials
Tamper-proof receipts for every action taken
Authorization
- Value Limits
Hard stops on max decision value per agent/role
- Delegation Chains
Track who authorized whom, maximum depth enforced
- Zero Trust
Every action verified, nothing assumed
Communication
- APEX Context
Shared memory and context for agent coordination
- Pub/Sub Channels
Secure topic-based messaging between agents
- Team Rooms
Agents form groups around shared outcomes
Self-Adapting
- Policy Evolution
Rules improve based on outcomes automatically
- Pattern Recognition
Surfaces insights humans would miss
- Self-Healing
Detects issues, proposes and applies fixes
Observability
- Real-Time Dashboards
Live view of all agent activity and decisions
- Performance Metrics
Agent, team, and org-level KPIs
- Anomaly Detection
Automatic alerting on unusual patterns
Traceability
- Decision Lineage
Full provenance for every choice made
- LLM Call Capture
Exact prompts and responses recorded
- Historical Query
Search across all decisions by any dimension
Auditability
- Cryptographic Proof
Tamper-evident logs for external audit
- 7-Year Retention
WORM storage for compliance requirements
- Reproducible Decisions
Replay any decision with exact same context
Coordination
- Human-in-the-Loop
Seamless escalation and feedback capture
- Team Formation
Agents group around shared outcomes dynamically
- Conflict Resolution
Automatic handling of competing goals
The Compound Effect
You Make Decisions
Approve, correct, override
System Learns
Extracts patterns, updates policies
Fabric Strengthens
More decisions automated
You Focus Higher
Strategy, not firefighting
Every interaction makes the system smarter. Your expertise becomes permanent infrastructure.
Decision Routing In Action
Watch a procurement decision flow through the mesh—from signal to execution, with tribal knowledge and human approval woven in.
Signal
Inventory AgentInventory Agent flags: "Widget SKU-7842 at 15% safety stock"
Accesses L1 cache: recent demand velocity Context
Context ManagerRetrieves supplier history, lead times, and tribal knowledge
L2 semantic: "GE Aviation chronically late—add 10% buffer" Policy Check
Governance EngineLoads org policy: "$25K+ requires procurement manager approval"
L3 policy store: spending authority limits Reasoning
Procurement AgentCalculates order: 500 units + 10% buffer = 550 units @ $52/unit = $28,600
L1 pricing: current contract rates Routing
OrchestratorValue exceeds $25K → escalates to human for approval
Confidence: 0.87, but value limit triggers HIL Human Review
Human-in-the-LoopManager approves, adds note: "Good catch on the buffer for GE"
Feedback captured → reinforces tribal knowledge policy Execution
Execution AgentPO generated, sent to supplier, receipt scheduled
L4 archive: full decision chain stored with VC Result
PO issued for 550 units. Tribal knowledge (GE buffer) applied. Value limit respected. Human approved. Full audit trail stored with verifiable credentials. Manager's positive feedback reinforces the GE policy for next time.
Knowledge That Compounds
Every decision captured. Every outcome tracked. Every correction becomes a policy. Here's what that looks like in practice.
Types of Logic We Capture & Improve
Tribal Knowledge
The unwritten rules your best people know. Captured from overrides, corrections, and notes.
Decision Playbooks
Multi-step reasoning patterns that work. Extracted from successful decision chains.
Policy Rules
Hard constraints and guardrails. Value limits, approval thresholds, compliance requirements.
Root Cause Analyses
What went wrong and why. Documented findings that prevent future failures.
The key: All of this is readable, writable, and improvable by the system—not locked in code or people's heads.
Real Examples from the Logic Layer
GE Aviation
Prevented 23 stockouts in Q3"Always order 10% extra—they chronically deliver late on POs"
supplier.ge_aviation.buffer_pct = 10 Firearms Inventory
Safety stock increased, fill rate up 18%"Demand cycles are 3x more volatile than standard SKUs"
category.firearms.safety_stock_multiplier = 1.8 Aerospace Fasteners
Zero quality escapes since policy adoption"Never substitute titanium grade—even if cheaper grade passes spec"
aerospace.titanium.substitution = BLOCKED Chemical Suppliers
Reduced expired write-offs by $47K/year"Shelf life data from vendor is optimistic by 15%—adjust expiry calculations"
chemicals.shelf_life_adjustment = 0.85 The Architecture That Makes It Work
Enterprise AI requires more than models. It requires infrastructure for trust, governance, and continuous improvement.
The Agentic Mesh
Connected Intelligence at Scale
A fabric of specialized AI agents, each with cryptographic identity, communicating through secure channels and coordinated by shared organizational goals.
Cryptographic Identity
Every agent has a unique, verifiable identity that cannot be spoofed or impersonated
Spine Communication
Agents communicate via secure pub/sub channels—like Slack for AI, with full audit visibility
Goal Alignment
Agents automatically discover and align with complementary goals across the mesh
Auto-Discovery
New agents self-integrate into the mesh with automatic hierarchy placement
Decision Lineage
Complete Provenance for Every Choice
Every decision traced back to its source—which agent, what data, which policies, what reasoning. Verifiable proof you can audit externally.
Verifiable Credentials
Tamper-proof receipts for every decision with cryptographic signatures
LLM Call Capture
Exact prompts and responses recorded—reproduce any decision for debugging
Delegation Chains
Track who authorized whom to make which decisions
Historical Query
Search across all decisions, filter by agent, policy, outcome, or time
Self-Improving Policies
Intelligence That Gets Smarter
Policies that track their own effectiveness and evolve based on outcomes. Underperforming rules are retired, successful patterns are promoted.
Citation Tracking
Monitor which policies agents actually use in their reasoning
Success Correlation
Link policy usage to decision outcomes—what works, what doesn't
Automatic Retirement
Low-performing policies are flagged and phased out
Pattern Promotion
High-success patterns are elevated to playbooks
Hierarchical Governance
Enterprise Structure for Enterprise AI
Policies cascade from organization to team to agent. Conflicts are detected and resolved automatically. Your hierarchy, enforced in code.
Org → Team → Agent
Three-tier governance with inheritance and override capabilities
Conflict Detection
Semantic analysis catches contradicting policies before they cause issues
Precedence Resolution
Clear rules for which policy wins when conflicts arise
Metrics Rollup
Performance flows up: agent → team → organization dashboards
Agents That Remember
AI agents are inherently stateless. Every call starts fresh. Without persistent memory, they rebuild context from scratch—wasting tokens and losing continuity.
Our solution: automatic tiered memory that promotes frequently accessed data to fast tiers and archives stale data to cold storage. Agents don't manage tiers—the infrastructure does.
Cost reduction from caching + compression
Audit retention with WORM storage
Memory Tier Architecture
Active Context
Current conversation, immediate decisions
Hot Cache
Recent memories, frequently accessed data
Semantic Index
Playbooks, searchable knowledge
Warm Storage
Documents, reference materials
Archive
Compliance, audit history
From Signal to Learning: Complete Lineage
Every decision flows through seven stages. At each stage, the system captures provenance, enforces governance, and records outcomes for continuous improvement.
Signal Detection
An agent detects a condition requiring decision—supply risk, demand shift, quality issue.
Policy Resolution
Relevant policies are loaded from hierarchy. Conflicts detected and resolved. Active set compiled.
Context Retrieval
Historical data, playbooks, and expert knowledge retrieved from memory tiers.
Reasoning
Agent applies reasoning with bounded LLM calls. Exact prompts and responses captured for audit.
Routing
Decision routed based on value, confidence, and policy. May escalate to human or executive agent.
Execution
Approved decisions trigger actions. Sandboxed execution for new or risky agents.
Learning
Outcomes recorded. Policy effectiveness updated. Expertise scores adjusted. System improves.
Infrastructure-Level Enforcement
Critical constraints aren't suggestions in a prompt—they're hard stops enforced before the LLM ever sees the request.
Value Limits
Agent authority level determines maximum decision value. Exceeds limit? Request blocked before LLM processing.
if value > agent.max_value: BLOCK Sandboxed Execution
New or risky agents execute in sandbox. Dangerous actions staged for approval. Full rollback capability.
sandbox_level: staged | full Complete LLM Audit
Every prompt, every response, cryptographically signed. Reproduce any decision. Prove what the AI actually said.
request_hash + response_hash Delegation Chains
Track who authorized whom. Cryptographic proof of delegation. Maximum chain depth enforced.
delegation_chain.verify() Confidence Routing
Low confidence? Automatic escalation. Below threshold? Human review required. Rules that evolve from outcomes.
if confidence < 0.6: escalate Policy Evolution
Nightly analysis of policy effectiveness. Retire underperformers. Promote winners. A/B test new rules.
PolicyCritic.evolve()
Build Your
Decision Factory
Data flows in. Logic improves. Decisions execute. Every cycle compounds. Agents with identity. Decisions with lineage. Policies that evolve. A system that makes more decisions, better decisions, faster—and gets smarter with every one.
Greater impact than isolated AI
Continuous optimization
Traceable & auditable
Compounding intelligence
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