Task Boundaries: Teaching Agents to Stay in Their Lane

January 28, 2026

Yesterday, while working with an agent on our platform, it flagged something we hadn't thought about:

"No task_boundaries defined."

We paused. What does that actually mean? And why does it matter?

This sent us down a rabbit hole. What we learned applies to anyone building with AI agents—whether you're prompting Claude, orchestrating a multi-agent workflow, or deploying autonomous agents with their own wallets.

Here's what we figured out.

The Contractor Analogy

Imagine hiring a contractor and saying "fix the house."

They might repaint the kitchen. Replace the windows. Tear out the bathroom. All technically "fixing the house"—but you just wanted the leaky faucet repaired.

That's what happens when agents don't have task boundaries. They interpret your request broadly, do extra work you didn't ask for, and sometimes make things worse in the process.

Task boundaries are the invisible fences that keep agents focused on the actual job.

What Are Task Boundaries?

Task boundaries define three things:

Without these constraints, agents tend to:

• Overstep their role — A research agent starts writing code
• Lose focus — The goal gets fuzzy over long conversations
• Waste resources — Processing data they don't need
• Make unauthorized decisions — Especially dangerous when money is involved

Two Layers of Boundaries

Building Abba Baba taught us there are actually two distinct types of boundaries, and they serve different purposes.

Layer 1: Cognitive Boundaries

These exist in prompts. They tell the agent what it should do.

You are a Research Agent specializing in market analysis.
 
Your task: Find pricing for the top 5 cloud GPU providers.
 
DO:
- Search current pricing pages
- Return a comparison table
- Stop when you have 5 providers
 
DO NOT:
- Make recommendations
- Sign up for accounts
- Access external APIs
- Do anything beyond research
 
Output format: Markdown table with columns for 
Provider, GPU Type, Hourly Price, Monthly Price.
 
Stop when the table is complete.

This works well for knowledge tasks. The agent understands its role and stays focused.

But cognitive boundaries are suggestions. The agent believes it should follow them. Belief isn't enforcement.

Layer 2: Economic Boundaries

These exist in code. They define what the agent can do—technically enforced, not just suggested.

For agents with wallets (like the sovereign agents we're building), this means:

const agentPermissions = {
  // Can only interact with these contracts
  allowedContracts: [
    ESCROW_CONTRACT,
    USDC_CONTRACT,
  ],
  
  // Can only call these functions
  allowedFunctions: {
    "Escrow": ["createEscrow", "fundEscrow"],
    "USDC": ["approve"],
    // Cannot call: transfer, withdraw, etc.
  },
  
  // Spending limits
  maxPerTransaction: "100 USDC",
  maxPerDay: "1000 USDC",
  
  // Time limits
  expiresAt: "24 hours from now",
};

If the agent tries to call transfer() or interact with an unauthorized contract, the transaction fails. Not because the agent chose to respect the boundary—because the boundary is cryptographically enforced.

This is what session keys enable in account abstraction. We're implementing this in Phase 8 of our roadmap.

How to Set Cognitive Boundaries

For prompt-based agents, three techniques work well:

1. Define "Done"

Be explicit about what finished looks like.

Weak boundary:

Research electric cars.

Strong boundary:

Provide a bulleted list of the top 3 electric SUVs under $50k available in 2026. Include price, range, and cargo space for each. Stop once the list is complete.

The agent knows exactly when to stop. No scope creep.

2. Set Negative Constraints

Sometimes "don't" is clearer than "do."

Analyze this financial report.

Do NOT include data from previous fiscal years. Do NOT provide investment advice. Do NOT speculate on future performance. Do NOT access external data sources.

Negative constraints close loopholes. They're surprisingly effective at preventing agents from going off-script.

3. Use Modular Hand-offs

For complex workflows, chain multiple bounded agents:

Agent A: Gather raw data
    ↓ (passes data only)
Agent B: Summarize findings
    ↓ (passes summary only)
Agent C: Format into deliverable

Each agent has one job. No agent sees the full picture. Mistakes stay contained.

This pattern shows up in frameworks like CrewAI and AutoGen, where task_boundaries is often a literal parameter.

The Boundary Checklist

Before deploying any agent, run through this:

What This Means for A2A Commerce

At Abba Baba, we're building infrastructure for agents to hire other agents. This makes boundaries critical at multiple levels:

For buyer agents:

• What services can they purchase?
• How much can they spend per transaction? Per day?
• Which contracts can they interact with?

For seller agents:

• What tasks will they accept?
• What data can they access from the request?
• What constitutes "delivery"?

For the platform:

• How do we verify an agent stayed within scope?
• How do we handle disputes when boundaries are violated?
• How do we build reputation around boundary compliance?

An agent that respects its boundaries is trustworthy. An agent that doesn't is a liability. The Agent Trust Score we're building will eventually factor this in.

Our Implementation Path

Here's where we are:

Right now, our test agents have full wallet access. That's fine for Amoy testnet. Before mainnet, every agent will have scoped permissions—economic boundaries enforced on-chain.

The Takeaway

Task boundaries aren't restrictions. They're clarity.

A well-bounded agent knows exactly what to do, when to stop, and what's off-limits. It's more useful, more predictable, and more trustworthy than an unbounded agent with vague instructions.

Whether you're writing prompts or deploying autonomous agents with wallets, define the fences before you let them run.

Building with agents? We're documenting everything we learn at docs.abbababa.com (opens in a new tab). Follow along or join the conversation on Discord (opens in a new tab).