Governing MCP and Agentic AI on AWS: Identity, Permissions, Observability, and Audit at Scale

Scenario

A platform team has dozens of internal agents using MCP tools and wants enterprise governance before customer-facing rollout.

Core problem

As agent count grows, risk shifts from “single model error” to “multi-agent control plane failure.” Governance must address:

• who can invoke which tools
• what data each agent can access
• how to observe every decision and action
• how to prevent cross-agent privilege escalation

AWS governance model

Use layered controls:

1. identity and credential boundaries (AgentCore Identity + IAM)
2. tool registry and permission policies
3. runtime observability and audit trails
4. human approvals for high-impact actions

Architecture

Step-by-step tutorial

1) Establish policy registry table

export AWS_REGION=us-east-1
export PROJECT=agent-governance

aws dynamodb create-table \
  --table-name ${PROJECT}-tool-policy \
  --attribute-definitions AttributeName=agent_id,AttributeType=S AttributeName=tool_id,AttributeType=S \
  --key-schema AttributeName=agent_id,KeyType=HASH AttributeName=tool_id,KeyType=RANGE \
  --billing-mode PAY_PER_REQUEST \
  --sse-specification Enabled=true

$env:AWS_REGION = "us-east-1"
$env:PROJECT = "agent-governance"

aws dynamodb create-table `
  --table-name "$($env:PROJECT)-tool-policy" `
  --attribute-definitions AttributeName=agent_id,AttributeType=S AttributeName=tool_id,AttributeType=S `
  --key-schema AttributeName=agent_id,KeyType=HASH AttributeName=tool_id,KeyType=RANGE `
  --billing-mode PAY_PER_REQUEST `
  --sse-specification Enabled=true

2) Policy evaluation component (FastAPI)

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

app = FastAPI(title="Agent Governance Policy API")

POLICIES = {
    "agent-support-1": {"tools": {"search_kb", "get_ticket"}, "max_risk": "medium"},
    "agent-ops-1": {"tools": {"search_logs", "open_incident"}, "max_risk": "high"}
}

class ToolRequest(BaseModel):
    agent_id: str
    tool_id: str
    requested_risk: str

@app.post("/authorize")
def authorize(req: ToolRequest):
    policy = POLICIES.get(req.agent_id)
    if not policy:
        raise HTTPException(status_code=403, detail="Unknown agent")
    if req.tool_id not in policy["tools"]:
        raise HTTPException(status_code=403, detail="Tool not permitted")

    levels = ["low", "medium", "high", "critical"]
    if levels.index(req.requested_risk) > levels.index(policy["max_risk"]):
        raise HTTPException(status_code=403, detail="Risk exceeds policy")

    return {"approved": True}

3) IAM least-privilege pattern

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": ["dynamodb:GetItem", "dynamodb:Query"],
      "Resource": "arn:aws:dynamodb:*:*:table/agent-governance-tool-policy"
    },
    {
      "Effect": "Deny",
      "Action": ["*"],
      "Resource": "*",
      "Condition": {"StringEquals": {"aws:RequestedRegion": "eu-west-3"}}
    }
  ]
}

Use deny conditions and scoped role sessions for tighter control.

4) Observability setup

• Emit decision logs: agent_id, tool_id, policy_result, reason, trace_id.
• Push metrics for denied requests and risky attempts.

aws cloudwatch put-metric-alarm \
  --alarm-name ${PROJECT}-policy-denials \
  --namespace AgentGovernance \
  --metric-name PolicyDenied \
  --statistic Sum --period 300 --evaluation-periods 1 --threshold 20 \
  --comparison-operator GreaterThanOrEqualToThreshold \
  --alarm-actions arn:aws:sns:us-east-1:123456789012:${PROJECT}-alerts

5) Human approval flow for high-impact tools

For tool categories like delete, transfer, publish, require explicit approval via Step Functions before execution.

Security recommendations

• isolate tool execution roles by trust zone
• maintain immutable policy version history
• enforce short-lived credentials
• bind user identity + agent identity for delegated actions

Cost optimization

• central policy engine reduces duplicate guardrails in every agent
• shared observability pipelines lower per-agent overhead
• evaluate heavy checks only on high-risk requests

Production checklist

• policy-as-code and peer review process in place
• identity model documented for users, agents, and tools
• denial and escalation runbooks tested
• quarterly access review and least-privilege verification
• audit export meets compliance retention requirements

References

• https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/identity
• https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/security-iam.html
• https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/observability-configure.html
• https://docs.aws.amazon.com/prescriptive-guidance/latest/govern-architect-agentic-ai/introduction.html