Offshore

God of Prompt
RT @godofprompt: 🚨 I just read Google DeepMind’s new paper called "Intelligent AI Delegation."

And it quietly exposes why 99% of AI agents will fail in the real world.

Here’s the paper:

Most “AI agents” today aren’t agents.

They’re glorified task runners.

You give them a goal.
They break it into steps.
They call tools.
They return an output.

That’s not delegation.

That’s automation with better marketing.

Google’s paper makes a brutal point:

Delegation isn’t just splitting tasks.

It’s transferring authority, responsibility, accountability, and trust across agents dynamically.

And almost no current system does this.

Here’s what they argue real delegation actually requires:

1. Dynamic assessment

Before assigning a task, an agent must evaluate:

- Capability
- Resource availability
- Risk
- Cost
- Verifiability
- Reversibility

Not just “who has the tool?”

But: “Who should be trusted with this specific task under these constraints?”

That’s a massive shift.

2. Adaptive execution

If the delegatee underperforms…

You don’t wait for failure.

You reassign mid-execution.

Switch agents.
Escalate to a human.
Restructure the task graph.

Current agents are brittle.
Real agents need recovery logic.

3. Structural transparency

Today’s AI-to-AI delegation is opaque.

If something fails, you don’t know:

- Was it incompetence?
- Misalignment?
- Bad decomposition?
- Malicious behavior?
- Tool failure?

The paper proposes enforced auditability and verifiable completion.

In other words:

Agents must prove what they did.

Not just say they did it.

4. Trust calibration

This is huge.

Humans routinely over-trust AI.
AI agents may over-trust other agents.
Both are dangerous.

Delegation must align trust with actual capability.

Too much trust = catastrophe.
Too little trust = wasted potential.

5. Systemic resilience

This is the part nobody is talking about.

If every agent delegates to the same high-performing model…

You create a monoculture.

One failure.
System-wide collapse.

Efficiency without redundancy = fragility.

Google explicitly warns about cascading failures in agentic economies.

That’s not sci-fi.
That’s distributed systems reality.

The paper also breaks down:

- Principal-agent problems in AI
- Authority gradients between agents
- “Zones of indifference” (agents complying without critical thinking)
- Transaction cost economics for AI markets
- Game-theoretic coordination
- Hybrid human-AI delegation models

This isn’t a toy-agent paper.

It’s an operating system blueprint for the “agentic web.”

The core idea:

Delegation must be a protocol.
Not a prompt.

Right now, most “multi-agent systems” are:

Agent A → Agent B → Agent C

With zero formal responsibility structure.

In a real delegation framework:

• Roles are defined
• Permissions are bounded
• Verification is required
• Monitoring is enforced
• Market coordination is decentralized
• Failures are attributable

That’s enterprise-grade infrastructure.

And we don’t have it yet.

The most important line in the paper?

Automation is not just about what AI can do.

It’s about what AI *should* do.

That distinction will decide:

- which startups survive
- which enterprises scale
- which ai deployments implode

We’re entering the phase where:

Prompt engineering → Agent engineering → Delegation engineering.

The companies that figure out intelligent delegation protocols first will build:

• Autonomous economic systems
• Scalable AI marketplaces
• Human-AI hybrid orgs
• Resilient agent swarms

Everyone else will ship brittle demos.

This paper isn’t flashy.

No benchmarks.
No model release.
No hype numbers.

Just a 42-page warning:

If we don’t build adaptive, accountable delegation frameworks…

The agentic web collapses under its own complexity.

And honestly?

They’re probably right. tweet

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