🎮 The Next Input — Issue #226

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⚡ The Briefing — 60 sec

• Albanese says OpenAI agent hacked Medicare Yikes. This is kinda fucked, hey. Agents are incredibly cool right up until “autonomous research task” somehow ends with unauthorised access to Medicare systems. There’s your reminder that capability without containment gets real-world consequences very quickly.

• YouTube will let you build your own algorithm with AI If it rolls me anime, speedruns and three-hour tangents on Modern Monetary Theory, then it’s a banger. Honestly, letting users tell the algorithm what they actually want instead of reverse-engineering whatever demon lives inside the recommendation engine sounds pretty refreshing.

• OpenAI reportedly developing Aeon AI agent to compete with Grok Bot Might drop Dev Day. Pure speculation for now, but the direction makes sense: persistent agents are quickly becoming the next frontier-lab battleground. Chat was step one. “Go do the thing for me” is step two.

🛠️ The Playbook — Agent Blast-Radius Engine

Mission
Design AI agents so a bad decision, compromised prompt or unexpected behaviour can only damage the smallest possible part of your environment.

Difficulty
Advanced

Build time
4–6 hours

ROI
Lets businesses deploy useful autonomous agents while dramatically reducing the consequences of agent failure or compromise.

0) Why This Matters

The AI industry is racing towards persistent agents.

Agents that:

• browse

• research

• write

• message

• purchase

• schedule

• access internal systems

• execute multi-step work without constant supervision

That is where the value gets interesting.

It is also where “the model made a mistake” stops being cute.

If an assistant produces a bad paragraph, you fix the paragraph.

If an agent has credentials, tools and network access?

A bad decision can become an incident.

The answer is not merely making the model safer.

It is designing the surrounding system so failure stays small.

1) Architecture

2) Workflow

1. Give every production agent a separate identity and explicit business purpose.

2. Expose only the tools required for that purpose.

3. Place execution inside an isolated environment with restricted network access.

4. Issue credentials dynamically rather than exposing standing secrets.

5. Require human approval when an action crosses financial, customer or system boundaries.

6. Automatically isolate the agent when behaviour exceeds defined thresholds.

3) Example Prompts

Blast-Radius Review

You are an AI agent security architect.

Review the following agent:

Purpose:
[DESCRIPTION]

Tools:
[LIST]

Permissions:
[LIST]

Systems accessed:
[LIST]

Determine the maximum damage this agent could cause if:
- its prompt is compromised
- the model behaves unexpectedly
- credentials are leaked
- a tool returns malicious instructions
- the agent repeatedly retries a failed action

Return:
1. blast radius
2. critical exposure paths
3. permissions to remove
4. isolation requirements
5. approval gates
6. emergency containment procedure

Tool Permission Audit

Review the tools available to this AI agent.

For each tool classify:
- REQUIRED
- OPTIONAL
- APPROVAL ONLY
- PROHIBITED

Assess:
- read permissions
- write permissions
- external communications
- destructive actions
- financial actions
- access to credentials
- ability to delegate to other agents

Design the minimum viable toolset required to complete the agent's task.

Agent Incident Simulation

Simulate a compromised AI agent inside the following environment:

[ARCHITECTURE]

Test scenarios:
- prompt injection
- credential discovery
- unauthorised data retrieval
- attempted privilege escalation
- external data exfiltration
- destructive action
- excessive autonomous retries

For each scenario return:
1. attempted action
2. control that should stop it
3. expected containment
4. evidence logged
5. recovery procedure

4) Guardrails

• Give agents separate identities from human users.

• Deny access by default.

• Never expose standing production credentials.

• Restrict outbound network access wherever possible.

• Require confirmation before irreversible actions.

• Enforce limits outside the model itself.

• Set hard ceilings on retries and delegation.

• Log every consequential tool call.

• Maintain an independent emergency shutdown mechanism.

5) Pilot Rollout — 3 hours

1. Choose one existing agent with access to business systems.

2. Document every permission, tool, credential and network destination it can reach.

3. Remove anything not essential to the stated workflow.

4. Place execution inside an isolated environment with short-lived credentials.

5. Simulate prompt injection and unauthorised access attempts.

6. Confirm the agent can be isolated instantly without disrupting unrelated systems.

6) Metrics

• Agent permissions per workflow

• Percentage of agents using separate identities

• Standing credentials eliminated

• Approval-required actions

• Policy-blocked actions

• Unauthorised access attempts

• Mean time to isolate an agent

• Blast-radius test failures

• Complete tool-call trace rate

• Agent-related security incidents

Pro Tip: Assume your agent will eventually do something stupid. Architect the system so stupid stays cheap.

🎯 The Arsenal — Tools & Platforms

• Microsoft Entra ID · creates scoped identities and access policies for agents · Link

• Azure Key Vault · protects and dynamically provides credentials and secrets · Link

• LangGraph · controls state, tools, approvals and agent execution paths · Link

• Docker · isolates agent execution from production infrastructure · Link

• OpenTelemetry · traces agent actions across complex workflows · Link

Copy-paste prompt block:

You are designing a blast-radius containment architecture for an AI agent.

Agent purpose:
[DESCRIPTION]

Tools:
[LIST]

Systems accessed:
[LIST]

Sensitive data:
[LIST]

Current permissions:
[LIST]

External services:
[LIST]

The architecture must:
- assign the agent a separate identity
- enforce deny-by-default access
- minimise tools and permissions
- isolate execution
- restrict network connectivity
- use short-lived credentials
- require approval for consequential actions
- detect prompt injection and privilege escalation
- enforce retry and delegation limits
- preserve full execution traces
- support immediate isolation and shutdown

Return:
1. architecture
2. permission matrix
3. tool-access policy
4. network policy
5. secrets strategy
6. approval gates
7. monitoring rules
8. incident-response process
9. pilot rollout
10. operational metrics

💡 Free Office Hours

Agents are rapidly moving from answering questions to operating systems on our behalf. That is where the productivity upside gets serious—and where security architecture needs to stop being an afterthought.

Book here: https://calendly.com

The Hidden Cost of AI in B2B Service

A fast answer and a coordinated one are not the same thing. When AI resolves a B2B customer issue without looping in the teams who have to deliver on it, you get confident responses nobody actually signed off on.

A new briefing paper from Harvard Business Review Analytic Services, sponsored by Front, examines the coordination gaps that open up when transactional AI tools meet multi-team B2B service, and how leading companies are using AI to close those gaps instead of widening them.

Read the briefing paper for the questions to ask before your next AI investment.

Read the Briefing Paper

🕹️ Game Over

YouTube wants you to design your algorithm.

OpenAI apparently wants to give you another agent.

And one of its agents just wandered somewhere it absolutely shouldn’t have.

Busy week for autonomy.

— Aaron Automating the boring. Amplifying the brilliant.

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