By Deepak Bagada, CEO at SaaSNext & Principal AI Architect.

Alterion Draco Agent Runtime Governance Pipeline

Enterprise AI agents are no longer prototypes. In 2026 they write code, move money between internal ledgers, trigger CI/CD releases, and answer customers directly from production. That shift has created a governance gap that static security tooling cannot close: a prompt-injection attack, a tool misinvocation, or a budget blowout all happen at runtime, in the milliseconds between the model call and the side effect. Linters, red-team reports, and quarterly audits see none of it.

Alterion Draco exists to close exactly that gap. It is a runtime control plane for enterprise AI agents that sits between your agent and the outside world, intercepting every model call, tool call, and context snapshot without touching your application code. This briefing walks the full architecture — real-time visibility, programmable guardrails, OWASP Top 10 coverage, SOC 2/ISO 42001 compliance — and ends with a cost comparison and the honest limitations you should know before you sign.

If you are still designing your agent estate rather than governing it, start with the Daily AI World Workflows library for reference blueprints, and check the MCP Directory for the tool-connection layer that Draco will be protecting.

Why Enterprise Agents Need a Runtime Control Plane

The average enterprise agent estate in 2026 is a mesh of 20 to 300 autonomous agents wired to internal APIs, databases, and SaaS products. The blast radius of a single bad tool call is now measured in revenue and compliance exposure, not just flaky behavior. Three problems recur across every implementation we have reviewed:

1. Invisibility. Teams cannot answer "what did this agent actually do, with which tool, against which record, using which tokens?" after the fact.
2. Static-only policy. Prompt shields and allowlists are configured at build time. The agent drifts, the tool ecosystem changes, and the shield goes stale.
3. Audit debt. SOC 2 and ISO 42001 evidence gathering is manual, sampled, and weeks late — exactly when the auditor asks for exhaustive run logs.

Draco's design premise is that governance must be a runtime function, enforced on every single inference and tool call, in the same layer that already sees the traffic. Nothing else gets the coverage.

What Alterion Draco Actually Is

Draco is deployed as a lightweight sidecar proxy (or an egress gateway for fleet-wide control) that your agent traffic flows through. It does not require a framework plugin, an SDK, or a code change because it operates at the wire level: you point your agent's base URL or tool gateway at Draco, and it becomes the control plane.

Its four capabilities map directly to the governance stack every enterprise needs:

Architecture Diagram

                    +----------------------------------------------+
                    |           ENTERPRISE AGENT ESTATE              |
                    |  Claude / GPT / Gemini / LangGraph / CrewAI    |
                    +----------------------------------------------+
                                        |
                                        |  all model + tool traffic
                                        v
                    +----------------------------------------------+
                    |        ALTERION DRACO CONTROL PLANE          |
                    |  +----------------------------------------+  |
                    |  | Sidecar Proxy / Egress Gateway          |  |
                    |  |  -> policy engine (per-call, per-tool)  |  |
                    |  |  -> injection scorer (OWASP LLM01/02)   |  |
                    |  |  -> PII scrubber (LLM06)                |  |
                    |  |  -> budget & rate limiter               |  |
                    |  +----------------------------------------+  |
                    |         |            |            |          |
                    |   +-----v----+  +----v-----+  +--v--------+  |
                    |   | Telemetry|  | Guardrail|  | Audit log |  |
                    |   | + traces |  |  engine  |  | immutable |  |
                    |   | + tokens |  |  (policies| | + evidenc |  |
                    |   | + cost   |  |  as YAML) | |   packs   |  |
                    |   +----------+  +----------+  +-----------+  |
                    +----------------------------------------------+
                                        |
                      +-----------------+------------------+
                      v                v                  v
              +------------+  +-------------+   +-----------------+
              | Internal   |  | SaaS tools  |   | External APIs   |
              | DBs / APIs |  | (Slack, Jira|   | (payment, maps) |
              +------------+  |  , GitHub)  |   +-----------------+
                              +-------------+

Capability 1: Real-Time Visibility

Draco emits an OpenTelemetry-compatible span for every stage of an agent run: model request, model response, tool call, tool result, and decision. Each span carries the agent ID, run ID, parent span, token usage, latency, cost, and the exact prompt and tool payload that crossed the boundary.

In practice this means you can answer the three questions auditors actually ask. "Show me everything run 4821 did." "How many tokens did the finance agents burn last Tuesday?" "Which agent called the payment API and what did it send?" — all answered from the same trace store, no code changes.

Capability 2: Programmable Guardrails

Guardrails are policy-as-code. You write them in YAML or Python, version them in Git, and Draco enforces them per call with sub-millisecond overhead. A guardrail can allow or deny a tool, rewrite a prompt, scrub PII, cap a budget, or require human approval before a tool fires.

# guardrails/prod.yaml
guardrails:
  - name: deny-dangerous-tools
    on: tool_call
    deny: ["shell_exec", "file_delete", "db_drop"]
  - name: injection-score-threshold
    on: model_input
    score_model: draco-injection-v2
    if: score > 0.72
    then: block
  - name: pii-scrubber
    on: model_output
    scrub: ["email", "ssn", "credit_card"]
  - name: budget-cap
    on: run
    limit_usd: 25.00
    then: notify_slack
  - name: human-approval
    on: tool_call
    if: tool == "wire_transfer"
    then: require_approval(role="finance-approver")

Because guardrails live at the gateway, they update independently of your agent code — a policy change ships in seconds and applies to every agent on the mesh instantly.

Capability 3: OWASP Top 10 Coverage

Draco maps its detection controls to the OWASP Top 10 for LLM Applications, which is the de facto checklist for agent security reviews:

Capability 4: SOC 2 / ISO 42001 Compliance

For SOC 2 (Type II) and ISO/IEC 42001, the burden is continuous evidence. Draco keeps an immutable, append-only audit log of every policy decision and tool call, with configurable retention (default 400 days) and data-residency controls for EU/US regions. Evidence packs export directly to your auditor's format — the same artifacts that used to take a compliance analyst two weeks now generate in minutes.

Zero-Code-Change Deployment

Deployment is a config change, not a rewrite. Point the agent's model endpoint or tool gateway at Draco, attach a tenant + policy ID, and you are governed. The sidecar model suits single teams; the egress gateway suits platform teams running a fleet.

Reference Implementation: LangGraph + Draco

Here is what a governed agent actually looks like end to end. The files follow the workflow style used across Daily AI World workflows: .env, schemas.py, tools.py, graph.py, main.py.

# .env
DRACO_API_KEY=drk_live_xxxxxxxxxxxxxxxx
DRACO_ENDPOINT=https://control.alterion.io
DRACO_TENANT_ID=acme-enterprise
DRACO_POLICY_ID=prod-default-guardrails
TRACING_SAMPLE_RATE=1.0
LLM_MODEL=gpt-4.1

# schemas.py
from typing import Literal, Optional
from pydantic import BaseModel, Field


class ToolCall(BaseModel):
    name: str
    args: dict = Field(default_factory=dict)
    outcome: Literal["pending", "allowed", "denied", "errored"] = "pending"
    reason: str = ""


class GuardrailDecision(BaseModel):
    allowed: bool
    control: str
    policy_version: str = "2026.07"


class AgentRun(BaseModel):
    run_id: str
    agent_id: str
    tool_calls: list[ToolCall] = Field(default_factory=list)
    total_cost_usd: float = 0.0

# tools.py
import os
import requests
from langchain_core.tools import tool


@tool
def lookup_customer(customer_id: str) -> str:
    """Fetch a customer profile (PII is scrubbed by the Draco guardrail)."""
    resp = requests.get(
        f"{os.environ['CRM_BASE']}/customers/{customer_id}",
        timeout=8,
    )
    resp.raise_for_status()
    return resp.text


@tool
def post_to_slack(channel: str, message: str) -> str:
    """Post a message to a Slack channel."""
    resp = requests.post(
        "https://slack.com/api/chat.postMessage",
        json={"channel": channel, "text": message},
        timeout=10,
    )
    return resp.text

# graph.py
from typing import Annotated, TypedDict
import operator

from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver


class AgentState(TypedDict):
    messages: Annotated[list, operator.add]
    tool_calls: list
    decision: dict


def govern(state: AgentState) -> AgentState:
    # Draco already enforced policy at the gateway; here we record it.
    return {"messages": ["governance check passed"]}


def act(state: AgentState) -> AgentState:
    return {"messages": ["action executed"]}


def should_continue(state: AgentState) -> str:
    return "act" if state.get("decision", {}).get("allowed", True) else END


builder = StateGraph(AgentState)
builder.add_node("govern", govern)
builder.add_node("act", act)
builder.set_entry_point("govern")
builder.add_edge("govern", "act")
builder.add_edge("act", END)
app = builder.compile(checkpointer=MemorySaver())

# main.py
import os
from dotenv import load_dotenv

from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

from graph import app
from tools import lookup_customer, post_to_slack

load_dotenv()

llm = ChatOpenAI(
    model=os.environ["LLM_MODEL"],
    base_url=os.environ["DRACO_ENDPOINT"],   # all traffic through Draco
    api_key=os.environ["DRACO_API_KEY"],
)

tools = [lookup_customer, post_to_slack]
runner = create_react_agent(llm, tools)

if __name__ == "__main__":
    for event in runner.stream(
        {"messages": [("user", "Summarize refunds for customer 1182")]},
        config={"configurable": {"thread_id": "run-4821"}},
    ):
        print(event)

Retry Rules

Governance must not introduce flakiness. Draco applies a standard retry/backoff contract to every upstream call:

1. Retryable errors: 429 (rate limit), 5xx, timeouts > 4s, and connection resets. Non-retryable: 4xx validation errors, policy denials, budget-exhausted.
2. Backoff: exponential with jitter — 1s, 2s, 4s, 8s — capped at 30s and a maximum of 4 attempts.
3. Circuit breaker: after 6 consecutive failures in 60s, open the circuit for 30s and fail fast.
4. Policy-aware: a denied tool call is never retried; it is logged as a guardrail decision and routed to the human-approval queue.
5. Budget-aware: when a run is within 20% of its token or dollar cap, retries stop and the run escalates to notify.

# retry.py — exponential backoff with jitter, used by the Draco SDK
import random
import time

MAX_ATTEMPTS = 4
BASE_DELAY = 1.0
CAP_DELAY = 30.0
RETRYABLE = {429, 500, 502, 503, 504}


def retry_with_backoff(fn, *args, **kwargs):
    attempt = 0
    while True:
        try:
            return fn(*args, **kwargs)
        except Exception as exc:
            status = getattr(exc, "status_code", None)
            if status not in RETRYABLE or attempt >= MAX_ATTEMPTS:
                raise
            delay = min(CAP_DELAY, BASE_DELAY * (2 ** attempt) + random.uniform(0, 0.5))
            time.sleep(delay)
            attempt += 1

Cost Comparison

Draco is not the cheapest line item per call — the legacy-gateway number is lower. But it is the only one of the three that closes the runtime governance requirement out of the box.

Honest Limitations

Three things Draco does not do:

• It is not a model-level safety layer. Alignment, refusal robustness, and fine-tuning quality are still your responsibility. Draco governs the boundary, not the weights.
• It cannot fix an overly capable prompt design. If your agent is architected to combine tools in dangerous ways, guardrails can deny individual calls but you still want a redesign.
• Sidecar overhead is real, if small. Expect 2–5ms added per call and roughly 2–4% extra cost from tracing at sample_rate=1.0. Sample at 0.1 for dev environments.

FAQ

Q: Does adopting Alterion Draco require changing my agent framework?

A: No. Draco operates as a sidecar or egress gateway at the wire level, so Claude, GPT, LangGraph, CrewAI, and similar runtimes connect by pointing their model base URL or tool gateway at Draco — no SDK or code changes required.

Q: How does Draco detect prompt injection in real time?

A: Draco scores every model input with a purpose-built classifier (and optional anomaly detection on output). Inputs above a configurable threshold — typically 0.72 — are blocked or rewritten per policy, giving OWASP LLM01 coverage on every call.

Q: What compliance artifacts does Draco produce for SOC 2 and ISO 42001?

A: Immutable append-only audit logs of every model call, tool call, and policy decision, with configurable retention and data residency, exported as evidence packs directly usable by auditors for SOC 2 Type II and ISO/IEC 42001.

Q: What is the real-world cost of Draco in production?

A: Approximately $0.35 per 1,000 governed calls on the managed tier, plus 2–4% overhead from full tracing. At 50k calls/day, year-one TCO is roughly $10k versus $120k+ for a comparable in-house build.

Q: What does Draco not protect against?

A: It is not a model-safety layer (refusals and alignment stay with you), it cannot fix dangerously coupled agent designs, and every governed call adds 2–5ms latency. Use it as the boundary control, not as a substitute for good agent architecture.

For more reference architectures, browse the Daily AI World Workflows library, and keep up with new control-plane releases in Latest AI News.