A Practical Reference Architecture for Agentic AI Controls: Gateway/Sidecar + Evidence Pipeline

When orgs say they want "AI governance", they usually mean one of two things:

Policies and process (reviews, standards, committees)
Model-level safety features (filters, guardrails, prompt patterns)

Both matter.

But neither is sufficient once you deploy agents that can take real actions.

What you need is a third thing:

An operational control plane at runtime.

The Architecture Pattern That Actually Scales

For agentic systems, the most durable pattern looks like this:

Agent → Control Point (Gateway/Sidecar) → Tools/APIs → Outcomes → Evidence Pipeline

Think of it like a service mesh for agent actions:

You don't trust every service to self-police
You enforce policy at the boundary
And you generate consistent telemetry and evidence

Reference Architecture: Gateway/Sidecar + Evidence Pipeline

The following diagram shows the complete reference architecture for agentic AI controls—from policy authoring through runtime enforcement to evidence capture.

flowchart TB
  %% Reference Architecture: Gateway/Sidecar + Evidence Pipeline

  subgraph Clients["Agent Callers"]
    A1["Agent App / Workflow"]
    A2["Multi-Agent Orchestrator"]
  end

  subgraph ControlPlane["FuseGov Control Plane"]
    P1["Policy Authoring<br/>Controls, Risk Tiers"]
    P2["Policy Registry<br/>Versioned Bundles"]
    P3["Key Mgmt / Signing<br/>Attest Bundle Integrity"]
  end

  subgraph Runtime["Runtime Enforcement Layer"]
    direction TB

    subgraph OptionG["Option A: Central Gateway"]
      G1["Agent Gateway<br/>(Policy Enforcement Point)"]
      G2["Stage 1: Deterministic Checks<br/>IAM, scopes, allowlists, rate limits"]
      G3["Stage 2: Semantic Verification<br/>Intent / context checks"]
      G4{"Degraded Mode?"}
    end

    subgraph OptionS["Option B: Sidecar per Agent"]
      S1["Agent Runtime"]
      S2["Sidecar PEP<br/>(Intercept Tool Calls)"]
      S3["Stage 1: Deterministic Checks"]
      S4["Stage 2: Semantic Verification"]
      S5{"Degraded Mode?"}
    end
  end

  subgraph Tools["Tooling / Action Surface"]
    T1["Internal APIs"]
    T2["SaaS APIs"]
    T3["Databases"]
    T4["Cloud Control Plane"]
    T5["Human Systems<br/>(Ticketing, Email)"]
  end

  subgraph Evidence["Evidence Pipeline"]
    E1["Decision Events<br/>(Allow/Deny/Escalate + Rationale)"]
    E2["Action Telemetry<br/>(What was called, when, where)"]
    E3["Outcome Verification<br/>(What changed)"]
    E4["Evidence Pack Builder<br/>(Normalize, Hash/Sign, Bundle)"]
    E5[("Immutable Evidence Store")]
    E6[("SIEM")]
    E7[("GRC / Audit")]
    E8[("Analytics / Data Lake")]
  end

  %% Policy distribution
  P1 --> P2
  P3 --> P2
  P2 --> G1
  P2 --> S2

  %% Traffic paths
  A1 --> G1
  A2 --> G1

  A1 --> S1
  S1 --> S2

  %% Gateway pipeline
  G1 --> G2 --> G3 --> G4
  G4 -->|Proceed| Tools
  G4 -->|Block| Tools

  %% Sidecar pipeline
  S2 --> S3 --> S4 --> S5
  S5 -->|Proceed| Tools
  S5 -->|Block| Tools

  %% Evidence emission
  G1 --> E1
  G2 --> E1
  G3 --> E1
  S2 --> E1
  S3 --> E1
  S4 --> E1

  Tools --> E2
  Tools --> E3

  E1 --> E4
  E2 --> E4
  E3 --> E4

  E4 --> E5
  E4 --> E6
  E4 --> E7
  E4 --> E8

Where the Control Point Sits

You have two deployment options (and most enterprises use both):

1) Gateway Pattern (Centralized)

A single policy enforcement gateway between agents and tools.

Aspect	Details
Best for	Standardization, centralized policy control, multi-agent environments
Deployment	Single cluster/service, all agent traffic routes through
Trade-offs	Simpler ops, but potential bottleneck; single policy version

2) Sidecar Pattern (Distributed)

A sidecar injected next to each agent runtime / workload.

Aspect	Details
Best for	Segmentation, autonomy, least privilege, multi-tenant isolation
Deployment	Per-agent or per-workload, Kubernetes-native
Trade-offs	More flexible, but harder to observe centrally

Both do the same core job: Intercept tool calls, evaluate policy, then allow/deny/transform.

What Happens at Runtime: Two-Stage Decisioning

In practice, you need two layers:

Stage 1: Deterministic Enforcement (Fast, Reliable)

Identity / attestation checks
Tool allowlists, scopes, rate limits
Data classification constraints
Budget/token/cost caps
Required approvals (if risk tier demands it)

Stage 2: Semantic Verification (Context-Aware)

Intent checks ("does this action match approved purpose?")
Policy interpretation where natural language is unavoidable
Anomaly detection across sequences of actions

If Stage 2 fails or becomes uncertain:

Degraded mode kicks in:

Block high-risk actions
Require approval
Or route to safe alternatives

flowchart LR
    A["Tool Call Request"] --> B["Stage 1<br/>Deterministic"]
    B -->|Pass| C["Stage 2<br/>Semantic"]
    B -->|Fail| D["DENY"]
    C -->|Pass| E["ALLOW"]
    C -->|Uncertain| F["Degraded Mode"]
    F --> G["Block / Approve / Safe Alt"]

The Evidence Pipeline (The Part Most Stacks Forget)

Enforcement without evidence is just another claim.

So every decision emits structured events into an evidence pipeline:

Event Type	What's Captured
Decision logs	Allow/deny + reason
Policy bundle versioning	Which rules evaluated
Input/output hashes	Optional for sensitive payloads
Human approvals	Escalation trail
Exception handling	Degraded mode activations
Post-action verification	What changed, what was touched

From There You Can Route To:

SIEM — Security monitoring
GRC tooling — Control testing / audit
Data lake — Analytics + drift detection

Why This Architecture Wins Politically Inside Enterprises

Because it separates concerns cleanly:

Team	Responsibility
AI teams	Ship features and agents
Security	Sets policy and risk tiers
GRC	Gets evidence without begging engineering
Audit	Gets repeatable artifacts, not screenshots

Implementation Considerations

Policy Distribution

flowchart LR
    A["Policy Author<br/>(Security/GRC)"] --> B["Policy Registry"]
    B --> C["Signed Bundle"]
    C --> D["Gateway"]
    C --> E["Sidecar 1"]
    C --> F["Sidecar N"]

Policies are versioned and signed
Control points pull or receive push updates
Bundle integrity is attested before enforcement

Evidence Chain Integrity

Every evidence pack includes:

{
  "event_id": "evt_9k2m4n",
  "timestamp": "2026-01-09T01:33:00Z",
  "policy_version": "2.1.0",
  "stage_1_result": "PASS",
  "stage_2_result": "PASS",
  "decision": "ALLOW",
  "hash": "sha256:b4f7...",
  "previous_hash": "sha256:a3f2..."
}

The previous_hash creates an immutable chain—tampering is mathematically detectable.

The Punchline

If agentic AI is the new "automation workforce", then:

Gateway/sidecar controls + evidence pipeline is the minimum viable safety architecture.

Anything less is hoping your policies will behave like runtime controls.

Getting Started Checklist

Decide deployment pattern: Gateway, Sidecar, or Hybrid
Define risk tiers for your agent actions
Author initial policy bundle with allowlists/scopes
Configure Stage 1 deterministic checks
(Optional) Enable Stage 2 semantic verification
Connect evidence pipeline to SIEM/GRC
Test degraded mode behavior
Go live with monitoring

This post is part of the FuseGov Reference Architecture series. The Gateway/Sidecar + Evidence Pipeline pattern represents the foundational deployment model for operational AI governance.

A Practical Reference Architecture for Agentic AI Controls