You Should Be Managing Your AI Agents as Engineers: Here's Why

The moment you add AI agents to your workflow, you’re no longer just a builder.

The creative maker part of the work evolves into something that looks a lot like engineering leadership.

Because it is.

We already know what happens when badly-led teams ship without specs, onboarding, decision architecture, or reviews.

Agents just do it faster.

If your agent's work gets erratic, or the output goes sideways, the tech isn’t the problem. The management is.

Hey, I’m Karo 🤗

AI Product Manager, and builder. I write Product with Attitude, a newsletter about building with AI and developing critical AI literacy through practice.

This is a guest post by.

Plenty of people compare agents to junior hires, but Kacper’s Define-Deliver-Drive framework is one of the clearest takes I’ve seen on AI agent management.

Kacper writes Scramble IT: practical systems for engineering leaders who want to ship faster with less chaos.

His words below.

What’s Inside

How engineering management principles map onto AI agent workflows.

The three failure modes every team hits.

Define-Deliver-Drive: a framework for task briefs, WIP limits, and delegation.

A five-level autonomy ladder.

Five rules to start with tomorrow.

And why vibe coding is fine, but vibe management is where it breaks.

AI Agent Management Is Not Prompt Engineering

A better prompt ≠ better output.

Real management, whether your team is five humans or five agents, comes down to three things:

Clarity on what done looks like. No task starts without a verifiable definition of done.
Focus on one thing at a time. WIP limits apply to agents as much as to humans.
Ownership that doesn’t bounce. Decision rights must be assigned, not assumed.

I call it Define-Deliver-Drive. I’ve used it with human engineering teams for years, and now I use it with agents.

The engineering discipline (what LangChain calls “agent engineering”) and the context quality (what Anthropic calls “context engineering”) both matter. But neither is enough without the management layer this post covers.

As Shubham Saboo noted on March 27, 2026:

Everyone thinks running AI agents is a technical skill. It’s not. It’s a management skill.

He’s right.

Let’s go through all three parts of the framework.

Three Failure Modes (And Why They Are Not Prompt Problems)

Before the framework, we need to name the enemies.

Failure Mode 1: Ambiguous Success Criteria → Hallucinated Confidence

Fuzzy input can produce polished output. It looks done, so you ship it.

Then it breaks, or you realize that it was wrong in ways you didn’t think to check.

The agent did its job.

You just never told it what the job was or when it was done.

Failure Mode 2: Too Many Parallel Threads → Fast in Isolation, Broken Together

You run multiple agent tasks simultaneously, for example:

Agent 1: Refactors the auth module
Agent 2: Updates the API docs
Agent 3: Writes tests
Agent 4: Runs tests

Each one makes good progress and finishes independently.

Then you find out that the tests don’t match the refactored code, and the docs describe an API that no longer exists.

None of them integrate cleanly. Integration day becomes rewrite day.

WIP limits exist in engineering for a reason. We learned this lesson with human teams years ago: the more poorly managed the work in flight, the less work that lands. Agents just let you make this mistake faster.

Failure Mode 3: No Decision Rights → Constant Human Bottleneck

Delegation without decision power is hiring a runner and making them ask permission at every turn.

If every non-trivial step needs you to weigh in and the agent waits, you become the blocker.

The whole point of autonomous agents collapses.

‘‘Prompting better’’ will not fix this. Hoping the agents will figure it out is wishful thinking.

Giving agents a system is what makes delegation work.

The Define-Deliver-Drive Framework

1. Define: Make Done Unambiguous

Agents need more clarity than humans. Not less.

Human developers have calibrated uncertainty: they know when they're in familiar territory and when they're guessing.

AI tools are more likely to generate plausible-looking output confidently, regardless of correctness.

Our job is to minimize that risk before the task starts.

Here’s how.

1. Use Task Briefs

Goal: One sentence. What exists when this is done?

Context: What already exists. What the agent is working in.

Constraints: Libraries, style guide, performance limits, security rules, timebox.

Inputs: Links, files, examples the agent needs.

Output format: The exact artifact: file, PR, summary, table.

Success checks: How you’ll verify it worked: tests, diffs, review criteria.

Non-goals: Explicitly what not to touch.

Escalation triggers: When to stop and ask rather than proceed.

The last two fields are the ones teams consistently forget.

2. Use Non-Goals and Escalation Triggers in Every Brief

Non-goals stop scope creep:
Don’t refactor, just fix the bug prevents a 10-line fix from becoming a 200-line rewrite.
Don’t touch the database schema keeps a feature task from becoming a migration.
Don’t add new sections stops a draft from doubling in scope overnight.
Escalation triggers are how you get agents that know their limits.
If you’re about to do something not listed in the task brief, stop.
If you’re making an assumption to fill a gap in the spec, state the assumption first.
If the task takes more than X steps, check in before continuing.

3. Definition of Done for AI Agents: If You Can’t Verify It, It’s Not Done

For code: Tests pass, diff reviewed, no regressions.
For research: Sources cited, claims traceable, contradictions flagged.
For migrations: Rollback documented, edge cases tested, stakeholder notified.

If you can’t verify it, you haven’t defined done.

2. Deliver: Protect Your AI Agent’s Focus

This phase prevents failure mode #2.

More agents running in parallel does not mean more done.

Work-in-progress limits exist in human teams because context switching has a cost.

In AI agent workflows, that cost compounds. Every open thread is a merge conflict waiting to happen.

1. One Focused Pipeline

To prevent this, build one focused pipeline that runs each stage before the next, instead of five parallel agent explorations.

The rule: one agent, one task, one branch.

Explore → Plan → Execute → Verify → Package

Practical WIP Policy

Set a thread limit. A maximum number of agent threads per human that can be successfully managed by that human.
Gate new starts. No new task starts until the previous one is completed or deliberately set aside.
Keep deliverables small. The smaller the output, the easier it is to review properly.

The teams shipping reliably with agents aren’t running ten things at once. They’re running a couple of things well.

3. Drive: Delegate Autonomy with a Delegation Ladder

You want the agent to move autonomously.

But you haven’t decided what it can own, what requires your sign-off, and what it should never touch without a human in the loop.

So it either:

asks you about everything = you’re the bottleneck
or it touches everything = you’re in trouble

The fix is a Delegation Ladder: an explicit model of how much autonomy each type of task gets.

These AI agent autonomy levels map closely to academic frameworks from Knight-Columbia and the Cloud Security Alliance.

How To Climb The Delegation Ladder

Don’t jump straight to Level 4 or 5. Start at Level 2.
Only move up when you can reliably verify the agent’s work at the current level.

Next, create an Ownership Map.

How To Use The Ownership Map

An Ownership Map is a document that answers one question: for each type of task, does the agent own it, or does a human?

Write it in an MD file and share it with the agents, so they don’t need to infer ownership.

For example:

Agent owns: first drafts, status updates, reformatting, refactors, test generation, and changelog drafts.
Human owns: final approvals, strategy, and user data handling.
Red flags (always escalate): security, anything involving personal data, access permissions, anything you can't easily undo.

Five Rules That Improve Any AI Agent Workflow in 2026

No task without a definition of done.
If you can’t describe what “finished” looks like before the agent starts, the task isn’t ready.
One task at a time.
Don’t let the agent juggle multiple things at once. Focused work beats scattered work, even when the worker is an AI.
Keep deliverables small.
Give the agent one small piece to finish, not a massive batch. The bigger the output, the less carefully you’ll check it.
Always verify before accepting.
Use checklists, spot checks, or human review, especially for high-stakes work. Verification isn’t something you add after. It’s built into your definition of done.
Set clear escalation triggers.
Before the task runs, decide: at what point should the agent stop and ask you instead of continuing on its own? Write it in the brief.

These rules are the management layer.

While LangChain’s “agent engineering” and Anthropic’s “context engineering” provide the technical foundations, this framework provides the operational discipline that makes those foundations ship reliably.

Vibe Coding Is Fine. Vibe Management Is Where It Breaks.

Vibe coding is fine. We all need the speed it provides. High-quality engineering organizations are increasingly comfortable giving up line-by-line control over generated code.

Vibe management is a different problem entirely.

When you give fuzzy instructions, run too many tasks at once, and never define what the agent can decide alone, you get work that looks right but isn’t, results that don’t fit together, and an agent that either blocks you constantly or makes important calls you never approved.

The fix is better systems. It always has been. Long before any of us heard about LLMs.

Thanks for reading,

Kacper

If this resonated, I’d love to hear how you’re managing your agents right now. What’s breaking? What’s working?