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Stop Giving AI the Steering Wheel

#ai #agents #security #architecture

A Practical Checklist for Building Controllable AI Agents

AI agents are getting better at planning, reasoning, and completing tasks.
But when developers try to deploy them beyond demos, the same question keeps coming up:

Can I safely let this thing act on my system?

In most cases, the honest answer is no.

This post is not about why AI is “bad.”
It’s about how to build agents that are safe enough to run in production.

The Core Rule (If You Remember Only One Thing)

AI can analyze.
AI can suggest.
AI must not decide or execute final actions.

Everything below follows from this rule.

Why Most Agents Fail in Production

If your agent does any of the following, it’s not production-ready:

  • Executes real actions directly (money, infra, config, data mutation)
  • Produces different outcomes for the same input
  • Depends on hidden context or conversation history
  • Can’t explain who approved an action
  • Keeps going when inputs are unclear instead of stopping

These are not edge cases.
They are structural flaws.

A Production-Ready Agent Architecture (Simplified)

Think in layers, not prompts.

[ AI Agent ]
     ↓
[ Structured Output ]
     ↓
[ Deterministic Decision Layer ]
     ↓
[ Human / Policy Veto ]
     ↓
[ Execution ]
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AI never skips layers.

The Controllable Agent Checklist

1️⃣ Agent Output Must Be Structured, Not Actionable

❌ Bad:

"Deploy the new config to production."
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✅ Good:

{
  "intent": "deploy_config",
  "risk_level": "high",
  "missing_info": ["rollback_plan"],
  "confidence": 0.72
}
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The agent describes reality.
It does not act on it.

2️⃣ Decision Logic Must Be Deterministic

Final decisions should come from code, not language.

❌ Bad:

if model_says_yes:
    deploy()
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✅ Good:

if risk_level == "high" and not approved:
    block()
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Same input → same output. Always.

3️⃣ Always Fail Closed

If something is unclear, stop.

❌ Bad:

  • Guess missing values
  • Try another tool
  • “Continue anyway”

✅ Good:

status = FAIL
reason = "Insufficient information"
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Silence or ambiguity is never permission.

4️⃣ No Direct Execution from the Agent

Never allow the agent to call:

  • trade()
  • deploy()
  • delete()
  • write_prod_config()

Agents propose.
Systems decide.

5️⃣ Human Approval Must Be Explicit and Logged

For high-risk actions:

  • Require a human approval step
  • Record who approved and when
  • Make approval non-bypassable

If no one can say “I approved this,”
the system should not run.

6️⃣ Every Decision Must Be Replayable

Ask yourself:

“Can I reproduce this decision tomorrow with the same inputs?”

If not, it’s not production-safe.

Replayability beats explainability.

A Simple Test You Can Run Today

Take your agent and ask:

  1. Can I stop it instantly?
  2. Can I replay its last decision exactly?
  3. Can I point to the human who approved it?
  4. Can I prove it would do the same thing again?

If any answer is “no,”
don’t give it execution rights.

Agents Aren’t Dead — Uncontrolled Agents Are

Agents are still extremely valuable:

  • Semantic parsing
  • Risk detection
  • Workflow coordination
  • Reducing human cognitive load

But the future belongs to controlled agents, not autonomous ones.

The smarter the agent,
the stricter the control layer must be.

Final Thought

Production systems don’t fail because AI is weak.
They fail because AI is trusted too early.

If you want your agent to survive outside demos,
take away the steering wheel and install real brakes.

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