ANCHOR: A Cognitive Middleware Layer for Internal Agentic AI Systems
Governing Reasoning, Not Data
Editor’s Note
This post contains the full text of a concept paper developed as part of my ongoing work on agentic AI governance, systems architecture, and decision accountability in defence and government contexts.
It is published here as a canonical reference for discussion and evaluation.
© 2025 Foresight Navigator. All Rights Reserved.
ANCHOR is a system-aware reasoning layer that governs how agentic systems operate across planning, operations, and automated pipelines ensuring they respect authority, escalation, and second-order effects.
Executive Introduction
Organizations are rapidly deploying internal large language models (LLMs) and agentic AI systems to support analysis, planning, policy development, operations, and decision-making. While these systems demonstrate impressive technical capability, a large percentage of AI initiatives fail to deliver durable value in high-stakes environments.
The root cause is not a lack of data, models, or workflows.
The failure arises because these systems reason without understanding the institutional constraints of the environments they operate within.
This paper proposes a cognitive middleware layer that sits above internal LLMs and agent runtimes to govern how reasoning occurs, where it must stop, and when human judgment is required. Strategic foresight is used as the primary stress test for this capability, not because foresight is the sole use case, but because it exposes reasoning failures earlier and more clearly than other domains.
1. What We Are Trying to Solve
Organizations are building internal agentic AI systems to assist with:
• analysis and synthesis
• planning and option development
• policy drafting
• operational decision support
The problem is not that these systems lack access to information or tools.
The problem is that they reason as if they exist outside the institutional system they serve.
As a result, many AI initiatives fail not because the technology is weak, but because the agentic systems:
• cross authority boundaries they do not understand
• collapse analysis into recommendations prematurely
• over-express confidence under uncertainty
• ignore escalation dynamics, institutional friction, and second-order effects
• behave as if access equals permission, and permission equals legitimacy
At scale, this produces false confidence, which is significantly more dangerous than slow or incomplete decision-making.
2. Why This Problem Exists
Most agentic AI platforms are designed around task execution, not decision accountability.
They optimize for:
• workflow completion
• tool orchestration
• speed and autonomy
• usability for builders
However, high-stakes organizations such as defence, government, and regulated enterprises operate under fundamentally different constraints:
• authority is layered, contextual, and situational
• some judgments cannot be automated
• uncertainty must be surfaced explicitly, not hidden behind fluent language
• stopping or deferring is often more important than answering
• decisions must remain defensible months or years after they are made
The gap is not technical capability.
The gap is governed reasoning.
3. What We Are Building Instead
We propose a cognitive middleware layer for internal agentic systems.
This is:
• not a model
• not a data platform
• not RAG
• not another agent framework
This layer sits above internal LLMs and agent runtimes and enforces:
• how agents are allowed to reason
• where reasoning must stop
• what must be surfaced before outputs are accepted
• which decisions require explicit human judgment
• how uncertainty, escalation, and authority are expressed
In practical terms:
This capability sells logic to LLMs, not access to data.
4. Why Foresight Is the Primary Stress Test
Strategic foresight is where these failures become visible fastest.
Foresight work:
• operates under deep uncertainty
• spans multiple domains and timelines
• tempts premature recommendations
• involves second- and third-order effects
• intersects with escalation, alliance dynamics, and adversary behavior
In foresight contexts, poor reasoning fails loudly.
However, the same failure modes appear across many domains, including:
• operational planning
• policy analysis
• procurement and capability development
• cyber response and crisis management
• HR, legal, and financial decision support
Foresight is not the product.
It is the stress test that demonstrates the necessity of the layer.
5. What This Layer Solves
This cognitive middleware layer exists to prevent internal AI systems from:
• acting as unauthorized decision-makers
• blending analysis and recommendation without authority
• masking uncertainty behind fluent output
• over-relying on unstable tools or contaminated sources
• degrading over time as prompts decay and personnel rotate
It ensures that AI systems behave as accountable participants in institutional decision-making, rather than as confident oracles.
6. Architectural Overview
This capability is a control plane, not an application.
6.1 Cognitive Policy & Logic Layer
Encodes how reasoning must occur.
• Policy engine: Open Policy Agent (OPA), Cedar, or a custom policy DSL
• Versioned cognitive logic modules:
○ decision-rights enforcement
○ escalation thresholds
○ uncertainty forcing functions
○ second-order effects requirements
○ contamination boundaries (open web vs authoritative sources)
Git-based approval workflows and signed releases
This logic is external to prompts and survives staff rotation, drift, and model changes.
6.2 Agent Interceptor / Middleware Gateway
Sits between agent runtimes and final outputs.
Intercepts:
○ tool calls
○ prompt assembly
○ final responses
Enforces mandatory reasoning steps
Blocks, rewrites, or forces human handoff when constraints are violated
6.3 Tool Governance Layer
Controls how tools are used, not merely whether they exist.
Tool registry containing:
○ purpose, classification, authority limits
○ schema contracts
○ reliability metadata
Tool gateway that:
○ validates inputs and outputs
○ enforces least-action principles
○ logs tool trust and failure events
6.4 Model Access Layer
Normalizes access to approved models without owning them.
• Approved model routing ( internal LLMs, open-weight models)
• Safe system prompts and structured output enforcement
• No training on live operational data
• Output schema validation and repair
6.5 Decision Artifact & Assurance Layer
Produces decision-grade outputs, not chain-of-thought disclosures.
Required output sections:
○ assumptions
○ uncertainty
○ authority boundaries
○ escalation considerations
○ “what would change this assessment”
Dual logging:
○ protected audit trails
○ concise operational justification for users
6.6 Observability & Governance
Designed for auditability, not dashboards.
• OpenTelemetry tracing
• Policy intervention and enforcement logs
• “Agent stopped itself” events
• SIEM integration
• Regression testing against known failure scenarios
7. What This Capability Is Not
This layer is intentionally not:
• a BI tool
• a workflow builder
• a data analytics platform
• a foresight content generator
• a replacement for model vendors
It is the layer that makes internal agentic systems deployable without institutional risk.
Governing Automated Security Decisions in Allied DevSecOps Pipelines
ANCHOR’s Role in FVEY and Allied DevSecOps Environments
Agentic AI systems are increasingly embedded in DevSecOps pipelines across defence, government, and allied partner environments. These systems automate security-critical decisions that directly affect delivery timelines, operational readiness, and interoperability across Five Eyes (FVEY) and allied organizations.
In contemporary allied DevSecOps pipelines, agentic systems routinely:
• automatically prioritize vulnerabilities
• recommend mitigations and configuration changes
• trigger pipeline gates and enforcement actions
• propose remediation steps across shared tooling and environments
These capabilities deliver speed and scale. However, they also introduce new classes of risk that are not technical failures, but failures of reasoning under institutional and alliance constraints.
Where DevSecOps Agentic Systems Fail in Practice
Current DevSecOps automation is optimized for local technical correctness, not system-level decision integrity.
In allied environments, agentic systems generally lack the ability to:
• reason about operational trade-offs between security enforcement and mission delivery
• assess the readiness and operational impact of blocking or delaying builds
• understand cross-partner dependencies, shared infrastructure, and escalation dynamics
• distinguish between security risk (technical exposure) and systemic risk (cascading operational, alliance, or mission effects)
As a result, automated security decisions may be technically justified while strategically misaligned.
In practice, these failures are often misattributed to tooling friction, false positives, or process immaturity. In reality, they stem from the absence of governed reasoning embedded within the automation itself.
This is the gap ANCHOR is designed to address.
ANCHOR: Embedding Foresight-Grade Reasoning into Allied DevSecOps Runtime Decisions
ANCHOR extends cleanly into allied DevSecOps pipelines by embedding system-aware reasoning logic directly into agentic decision paths. This is achieved without introducing new data dependencies, modifying existing tools, or constraining delivery workflows.
The same reasoning logic modules used in planning and analytical contexts are reused at runtime in DevSecOps environments. These modules are domain-agnostic and portable across organizations.
Authority and Decision Rights Logic
ANCHOR enforces explicit decision boundaries within automated pipelines by requiring agentic systems to reason about authority before acting.
This includes determining:
• who has the authority to block, delay, or override a build
• under what conditions human intervention is mandatory
• when automated enforcement must stop and escalate
By encoding decision rights explicitly, ANCHOR prevents automation from assuming authority it does not possess, while preserving speed where automation is appropriate.
Escalation and Blast-Radius Reasoning
ANCHOR requires agentic systems to evaluate the broader implications of local security actions, including:
• local remediation versus system-wide impact
• ripple effects across allied and partner pipelines
• downstream mission, readiness, and interoperability consequences
This ensures that automated security decisions respect alliance-level dependencies and avoid actions that unintentionally degrade collective capability.
Uncertainty and Confidence Bounding
ANCHOR forces automated systems to explicitly surface uncertainty by distinguishing between:
• scanner confidence and real-world exploitability
• model-generated assessments and policy thresholds
• definitive findings and conditional judgments
Each automated decision must articulate what would change the assessment, preventing overconfidence driven by probabilistic or incomplete signals.
Second-Order Effects Logic
ANCHOR embeds structured reasoning about indirect and cumulative impacts, including:
• security hardening versus delivery and deployment timelines
• automation bias versus operator trust and adoption
• cumulative friction introduced across interconnected pipelines
This shifts DevSecOps automation from narrow optimization toward long-term system resilience and trust.
The Value ANCHOR Brings for Canada in Allied Environments
ANCHOR positions Canada as a reasoning integrator within allied DevSecOps ecosystems, rather than as a platform or tooling competitor.
Specifically, ANCHOR enables Canada to:
• deploy agentic DevSecOps automation without ceding decision authority
• align automated security enforcement with Canadian legal, policy, and operational constraints
• reduce alliance friction by making escalation logic explicit rather than implicit
• contribute a governance-grade reasoning layer that strengthens trust across FVEY and allied partners
Rather than duplicating or competing with allied platforms and tooling, ANCHOR complements them by providing a neutral, portable cognitive control layer that improves interoperability, accountability, and decision integrity.
Key Distinction
It is foresight-grade reasoning logic embedded in runtime systems.
ANCHOR ensures that automated security decisions in allied DevSecOps pipelines are made with awareness of authority, escalation, uncertainty, and second-order effects—capabilities essential for secure, interoperable, and mission-aligned automation.
8. Summary
This capability prevents internal AI systems from crossing decision boundaries they do not understand.
© 2025 Foresight Navigator
This post is shared for reading and discussion. Please credit Foresight Navigator if quoting or referencing. Commercial reuse or reproduction beyond brief excerpts requires permission.
ANCHOR™ and the original frameworks and system designs discussed here are the intellectual property of Foresight Navigator. References to third-party tools or standards remain the property of their respective owners.
This work is licensed under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 International License (CC BY-NC-ND 4.0).
