AI Gateway & Sidecar: Hybrid Runtime Governance

Most enterprises are landing in the same place with agentic AI:

Some agents run inside product teams, close to workloads and internal APIs.
Other agents operate across the org, calling shared tools, SaaS platforms, and cloud control planes.
Security and GRC need governance that's real at runtime, not just a policy doc.

That's why a hybrid runtime governance architecture wins in practice.

Not "central gateway only." Not "agent-local enforcement only." Both.

This post explains the hybrid reference architecture: Gateway + Sidecar + Evidence Pipeline—and why it's the minimum viable control plane for agentic systems in production.

The Core Idea: Control Planes, Not Guidelines

Agentic systems break traditional assurance because actions are:

Autonomous (not always human-triggered)
Dynamic (context-dependent)
Tool-driven (APIs, SaaS, cloud control planes)
Multi-step (chains of calls)

So the question becomes:

Where do we enforce policy, and how do we prove it happened?

The answer is a runtime control plane built from three layers:

Policy Lifecycle & Governance — how controls become deployable artifacts
Hybrid Enforcement — where policy is applied: gateway + sidecar
Evidence Pipeline — how proof is generated, stored, and exported

Complete Reference Architecture

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

  %% ===== Policy Lifecycle / Governance =====
  subgraph Gov["Policy Lifecycle & Governance"]
    direction TB
    R1["Policy-as-Code Repo<br/>Git / PR reviews"]
    R2["Approval Workflow<br/>CISO / GRC / SecArch"]
    R3["Policy Compiler<br/>+ Bundle Builder"]
    R4["Bundle Signing<br/>KMS / HSM"]
    R5["Policy Registry<br/>Versioned Bundles"]
    R6["Drift & Rollback<br/>deployed vs approved"]
    R1 --> R2 --> R3 --> R4 --> R5
    R5 --> R6
  end

  %% ===== Asset Registries =====
  subgraph Reg["Registries"]
    direction TB
    TR["Tool Registry<br/>(owner, risk tier, scopes,<br/>data classes, spend/rate caps)"]
    AR["Agent Registry<br/>(agent id, owner, allowed intents)"]
  end

  %% ===== Callers =====
  subgraph Callers["Agent Callers"]
    direction TB
    A1["Agent App / Workflow"]
    A2["Multi-Agent Orchestrator"]
  end

  %% ===== Hybrid Enforcement Layer =====
  subgraph Enforce["Hybrid Enforcement Layer"]
    direction LR

    subgraph GW["Central Gateway PEP"]
      direction TB
      G0["Gateway PEP<br/>Intercept Tool Calls"]
      G1["Stage 1: Deterministic<br/>IAM, allowlists, scopes, caps"]
      G2["Stage 2: Semantic Verification<br/>intent / context checks"]
      G3{"Mode"}
      G4["Observe-only"]
      G5["Enforce (Allow/Deny)"]
      G6["Escalate for Approval"]
      G0 --> G1 --> G2 --> G3
      G3 --> G4
      G3 --> G5
      G3 --> G6
    end

    subgraph SC["Sidecar per Agent PEP"]
      direction TB
      S0["Agent Runtime"]
      S1["Sidecar PEP<br/>Local Intercept"]
      S2["Stage 1: Deterministic"]
      S3["Stage 2: Semantic Verification"]
      S4{"Mode"}
      S5["Observe-only"]
      S6["Enforce (Allow/Deny)"]
      S7["Escalate for Approval"]
      S0 --> S1 --> S2 --> S3 --> S4
      S4 --> S5
      S4 --> S6
      S4 --> S7
    end
  end

  %% ===== Approval / Exception Handling =====
  subgraph Approvals["Approval & Exceptions"]
    direction TB
    H1["Step-up Auth<br/>high-risk approvals"]
    H2["Human Approval Workflow<br/>ServiceNow / Jira / Slack"]
    H3["Time-boxed Waiver / Exception<br/>compensating controls"]
    H1 --> H2 --> H3
  end

  %% ===== Action Surface =====
  subgraph Tools["Tooling / Action Surface"]
    direction TB
    T1["Internal APIs"]
    T2["SaaS APIs"]
    T3["Databases"]
    T4["Cloud Control Plane"]
  end

  %% ===== Evidence Pipeline =====
  subgraph Evidence["Evidence Pipeline"]
    direction TB
    E1["Decision Events<br/>allow / deny / escalate"]
    E2["Action Telemetry<br/>tool called, params meta"]
    E3["Outcome Verification<br/>what changed"]
    E4["Evidence Pack Builder<br/>normalize, hash, sign, bundle"]
    E5[("Immutable Evidence Store<br/>WORM - Append-only Log")]
    E6[("SIEM - SOAR")]
    E7[("GRC - Audit")]
    E8[("Data Lake - Analytics")]
    E1 --> E4
    E2 --> E4
    E3 --> E4
    E4 --> E5
    E4 --> E6
    E4 --> E7
    E4 --> E8
  end

  %% ===== Trust Signals =====
  subgraph Trust["Identity & Attestation Signals"]
    direction TB
    I1["Workload Identity<br/>(cloud workload identity)"]
    I2["Optional Attestation<br/>(runtime signals)"]
  end

  %% ===== Connections =====
  R5 --> G0
  R5 --> S1
  TR --> G1
  TR --> S2
  AR --> G2
  AR --> S3
  Trust --> G1
  Trust --> S2

  A1 -->|Preferred: Local tools| S0
  A2 -->|Shared/Enterprise tools| G0

  G5 --> Tools
  S6 --> Tools

  G6 --> Approvals
  S7 --> Approvals

  Approvals -->|Approved| G5
  Approvals -->|Approved| S6

  Tools --> E2
  Tools --> E3
  G0 --> E1
  S1 --> E1
  Approvals --> E1

1) Policy Lifecycle & Governance: Make Policies Deployable

Most governance stacks stop at "policy written."

In this architecture, policy is treated as a versioned, signed, deployable bundle.

Flow

Step	What Happens
Policy-as-code repo	Policies are reviewed like software (Git/PR reviews)
Approval workflow	CISO/GRC/SecArch sign-off with accountable owners
Policy compiler + bundle builder	Human-readable intent → machine-executable enforcement
Bundle signing	KMS/HSM signing makes policies tamper-evident
Policy registry	Runtime components fetch known-good versioned bundles
Drift & rollback	Prove what was deployed, detect drift, rollback safely

Why This Matters

Auditors don't just want your policy. They want to know:

Who approved it
What version was active at the time of an event
Whether runtime enforcement matched the approved control

2) Registries: Governance Needs an Inventory of "What Can Act"

Agents don't act in a vacuum. They act through tools.

So governance needs two registries:

Tool Registry (The "Action Surface" Catalog)

Each tool is registered with:

Attribute	Purpose
Owner	Accountability
Risk tier	Enforcement intensity
Allowed scopes/methods	Least privilege
Data classification rules	Data protection
Spend/rate caps	Cost control
Approval requirements	Escalation triggers

Agent Registry (Who Is Allowed to Do What)

Each agent is registered with:

Attribute	Purpose
Agent identity	Unique identifier
Owner/team	Accountability
Permitted intents/purposes	Scope bounding
Allowed tool groups	Access control

Why this matters: Most AI failures are not "bad models." They're unbounded actions executed through tools with excessive authority.

3) Hybrid Enforcement: Gateway + Sidecar (PEPs)

The enforcement layer has two Policy Enforcement Points (PEPs):

A) Central Gateway (For Shared or High-Risk Tools)

The gateway is ideal when:

Many agents share the same action surface
You need uniform enforcement
Tools are high impact (cloud control plane, financial ops, identity systems)
You want centralized visibility

Gateway flow:

flowchart LR
    A["Tool Call"] --> B["Stage 1<br/>Deterministic"]
    B --> C["Stage 2<br/>Semantic"]
    C --> D{"Mode"}
    D -->|Observe| E["Log Only"]
    D -->|Enforce| F["Allow/Deny"]
    D -->|Escalate| G["Human Approval"]

B) Sidecar per Agent (For Local, Low-Latency Enforcement)

Sidecars are ideal when:

You need per-agent segmentation
Workloads must keep working even if central services degrade
Teams operate independently
Latency and locality matter

Sidecar flow mirrors the gateway — deterministic checks, semantic verification, and mode selection.

Why Hybrid Wins

Pattern	Strength
Sidecars	Local control and resilience
Gateways	Central consistency and shared-tool governance
Together	Scale governance across a real enterprise, not a perfect diagram

4) Approvals & Exceptions: Reality, but Controlled Reality

If your governance model can't handle exceptions, it won't survive contact with operations.

This architecture includes a dedicated approval/exception lane:

Step-up auth for high-risk approvals
Human approval workflow (ServiceNow/Jira/Slack)
Time-boxed waivers with compensating controls

Crucially: Approvals and waivers are not "outside governance." They become structured evidence.

5) Tooling / Action Surface: Where Risk Actually Materializes

Agents interact with systems that have real-world consequences:

Internal APIs
SaaS APIs
Databases
Cloud control planes

This is where you need consistent enforcement—because the action surface is where:

Data can be exfiltrated
Privileges can be misused
Costs can spike
Irreversible changes can be made

6) Evidence Pipeline: Turn Every Decision Into Proof

Enforcement without evidence is just another claim.

So every runtime decision emits structured events into an evidence pipeline:

Event Type	What's Captured
Decision events	Allow/deny/escalate + rationale
Action telemetry	Which tool was called, metadata, scope
Outcome verification	What changed

Evidence Pack Builder

Normalizes events into a consistent schema
Hashes/signs them for integrity
Bundles by transaction/session/case

Evidence Outputs

Destination	Purpose
Immutable evidence store	Append-only / WORM-aligned
SIEM/SOAR	SOC monitoring
GRC/Audit	Control testing
Data lake/analytics	Drift, anomaly patterns, reporting

What Auditors Get

A consistent chain from:

Policy version → Enforcement decision → Action → Verified outcome → Immutable evidence

7) Trust Signals: Identity and (Optional) Attestation

To avoid "self-reported governance," runtime decisions must tie to real identities:

Signal	Purpose
Workload identity	Cloud workload identity verification
Optional attestation	Runtime posture, provenance indicators

These trust signals feed deterministic enforcement (Stage 1) so that:

"Who executed this action?" is always answerable
Enforcement can depend on verified runtime context

How to Use This Architecture in a Pilot

If you want to operationalize this quickly, do it in three moves:

1. Start in Observe-Only Mode

Capture decisions and evidence without blocking.

2. Register Tools + Set Risk Tiers

Make the action surface explicit.

3. Flip Enforcement on the Top 3 High-Risk Tools

Cloud control plane
Identity admin actions
Data export

Success Criteria (Measurable)

Metric	What It Tells You
% of tool calls evaluated against policy	Coverage
Deny rate + top deny reasons	Enforcement posture
Approval volume + time-to-approve	Escalation load
Evidence pack completeness	Coverage and retention
Drift events (policy vs runtime)	Configuration hygiene

The Takeaway

Hybrid governance isn't a "nice-to-have."

It's the first architecture pattern that matches how enterprises actually deploy agentic AI:

Distributed teams
Shared tools
Mixed risk tiers
Real audit expectations

Gateway + Sidecar + Evidence Pipeline turns governance into something that:

✅ Enforces at runtime
✅ Survives operational reality
✅ Produces evidence that stands up under scrutiny

Next Steps

If you're running agent pilots and want:

A sample Evidence Pack schema
A pilot checklist

Those are the fastest ways to operationalize this architecture.

Frequently Asked Questions

What is the difference between an AI Gateway and an AI Sidecar?

An AI Gateway is a centralized proxy that manages multiple agents and shared enterprise tools. An AI Sidecar is a decentralized enforcement point deployed directly alongside an individual agent runtime.

How does sidecar injection work for AI governance?

Sidecar injection involves deploying a security container (the Policy Enforcement Point) in the same pod or namespace as the AI agent. This allows for ultra-low latency interception of all tool calls and API requests.

What are the benefits of LLM gateway for enterprises?

An LLM gateway (or AI Gateway) provides centralized cost control, rate limiting, data redaction, and a consistent audit trail across all Large Language Model interactions within an organization.

Why use a hybrid approach to runtime security?

A hybrid approach (Gateway + Sidecar) allows organizations to balance central policy consistency for high-risk shared tools with per-team autonomy and resilience for local agent workloads.

This post is part of the FuseGov Reference Architecture series. The Hybrid Runtime Governance pattern represents the production-grade deployment model for enterprises with diverse agent workloads and real audit requirements.

Hybrid Runtime Governance for Agentic AI