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Ali Raza
Ali Raza

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6. AI Agents Are Not Replacing Developers Yet. They Are Creating New Problems to Debug.

#ai #productivity #webdev #programming

AI agents can write code, call tools, inspect repositories, and complete multi-step tasks. But as they become more autonomous, developers are discovering something unexpected: the agent itself is becoming another complex system that needs debugging.

AI agents are often presented as the next step in software development.

Instead of asking an AI to generate a function, developers can now give an agent a broader goal:

Find the bug, investigate the repository, update the code, run the tests, and create a pull request.

That sounds like a major step toward autonomous software development.

And in some situations, it is.

AI agents can already help developers explore codebases, write code, run commands, use tools, generate tests, and work through multi-step tasks. But the growing use of agents is revealing a new engineering reality.

The more autonomous an AI system becomes, the more complex its failures become.

A traditional bug might be relatively simple:

Input
  ↓
Function
  ↓
Unexpected Output
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An agent failure can look very different:

Goal
  ↓
Agent Plans an Action
  ↓
Agent Selects a Tool
  ↓
Tool Returns Information
  ↓
Agent Interprets the Result
  ↓
Agent Updates Its Plan
  ↓
Agent Takes Another Action
  ↓
Unexpected Outcome
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Now the developer has a difficult question:

Where exactly did the system go wrong?

Was the prompt unclear?

Was the context incomplete?

Did the agent misunderstand the goal?

Did it choose the wrong tool?

Did the tool return incorrect data?

Did the agent misinterpret the result?

Did its memory contain outdated information?

Did the model simply make a bad decision?

This is why AI agents are not eliminating debugging.

In many cases, they are creating an entirely new category of debugging problems.

AI Agents Are Useful, But They Are Not Yet Mainstream

Despite the excitement around autonomous AI systems, most developers are not using agents as their primary development workflow.

Stack Overflow's 2025 Developer Survey found that 52% of developers either do not use AI agents or stick to simpler AI tools, while 38% reported having no plans to adopt agents. At the same time, developers who do use agents report meaningful productivity benefits, with roughly 70% saying agents reduce the time spent on specific development tasks and 69% reporting increased productivity. ([Stack Overflow Developer Survey][1])

That creates an interesting picture.

AI agents are clearly useful.

But they are also introducing enough complexity that adoption remains uneven.

Microsoft Research reached a similar conclusion after studying developers working with software engineering agents. Its researchers found that agents can solve real software engineering tasks, but developers achieved better results when they actively collaborated and iterated with the agent rather than treating it as a one-shot autonomous system. Trust, debugging, and testing remained significant challenges. ([Microsoft][2])

The future may involve more agents.

But that does not necessarily mean fewer engineering problems.

It may mean different engineering problems.

Traditional Software Follows Rules. AI Agents Make Decisions.

One reason AI agents are difficult to debug is that they are not traditional deterministic programs.

Consider a normal function:

function calculateTax(price, rate) {
  return price * rate;
}
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Given the same input, developers expect the same behavior.

That makes debugging relatively straightforward.

You can inspect:

  • The input
  • The logic
  • The output

An AI agent is different.

Suppose an agent receives this instruction:

Investigate the failed payment issue and fix the problem.
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The agent might:

  1. Search the codebase.
  2. Inspect payment logs.
  3. Read API documentation.
  4. Form a hypothesis.
  5. Modify a function.
  6. Run tests.
  7. Discover another issue.
  8. Change its approach.

The system is making decisions throughout the process.

That flexibility is what makes agents useful.

It is also what makes them difficult to debug.

Anthropic's engineering research on multi-agent systems notes that agents can make dynamic decisions and behave non-deterministically between runs, even when working with identical prompts. This makes it harder to determine why a particular failure occurred. ([Anthropic][3])

The same task may not always produce the same sequence of actions.

And that changes debugging completely.

The Bug Is No Longer Just in the Code

When a traditional application fails, developers usually investigate the application.

When an AI agent fails, developers may need to investigate the entire decision process.

Consider this example.

A customer support agent receives the request:

My subscription was canceled, but I was still charged.

The agent has access to:

  • A customer database
  • Billing records
  • Subscription APIs
  • Support documentation

It gives the wrong answer.

Where is the bug?

Possible answers include:

The context was incomplete

The agent did not receive the latest billing record.

The retrieval system failed

The correct policy existed but was not retrieved.

The agent selected the wrong tool

It searched support documents instead of checking billing data.

The tool returned unexpected information

An API returned cached or outdated results.

The agent misunderstood the data

It retrieved the correct information but interpreted it incorrectly.

The instructions were ambiguous

The system did not clearly explain how to handle canceled subscriptions.

The model made a reasoning error

The available information was correct, but the conclusion was wrong.

Traditional debugging usually focuses heavily on implementation.

Agent debugging requires investigating behavior.

AI Agents Create Tool-Calling Problems

Tool use is one of the features that makes AI agents powerful.

An agent can potentially interact with:

  • Databases
  • APIs
  • Search engines
  • File systems
  • Code repositories
  • Browsers
  • Internal services

But every tool introduces another possible failure point.

Imagine an agent designed to investigate a production issue.

It has access to three tools:

Search Logs
Read Database
Check Deployment History
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The agent receives the goal:

Find why users cannot log in.

A successful workflow might be:

Check Recent Deployment
        ↓
Search Authentication Logs
        ↓
Compare Failed Requests
        ↓
Inspect User Data
        ↓
Identify Root Cause
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But the agent might instead:

Search Documentation
        ↓
Find an Old Authentication Article
        ↓
Assume It Is Relevant
        ↓
Modify the Wrong Service
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The tools worked.

The agent simply used them poorly.

This creates a new category of failure.

The problem is not whether a tool works. The problem is whether the agent knew when and how to use it.

Debugging an Agent Means Debugging a Chain of Decisions

One of the biggest changes introduced by AI agents is the need to inspect decision chains.

A developer may need to answer:

What did the agent know?
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What did it decide?
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Why did it choose that tool?
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What information did the tool return?
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How did the agent interpret that information?
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Why did it continue in that direction?
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This is much closer to investigating a process than debugging a single function.

Anthropic's research on agent observability describes this exact challenge. In production systems, simply knowing that an agent failed is often not enough. Engineers may need tracing that reveals search behavior, tool choices, failures, and decision patterns in order to identify the root cause. ([Anthropic][3])

This is why observability is becoming increasingly important in agent engineering.

Observability Is Becoming a Core Feature of AI Agents

Traditional applications are already monitored.

Developers track:

  • CPU usage
  • Memory
  • Error rates
  • Response times
  • Database performance

AI agents need some of those metrics too.

But they also need new forms of observability.

For example:

Agent Goal
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What was the agent trying to achieve?

Context
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What information was available?

Tool Calls
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Which tools did the agent use?

Decision Path
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What actions did it take?

Intermediate Results
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What happened after each action?

Token and Cost Usage
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How expensive was the task?

Final Outcome
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Did the agent actually complete the goal?

A 2026 survey from LangChain involving more than 1,300 professionals found that observability had become widely adopted in agent deployments, with nearly 89% of respondents reporting some form of agent observability. Quality was also identified as a major production barrier. ([LangChain][4])

That statistic says something important.

Teams are learning that you cannot reliably operate an agent you cannot inspect.

The Agent Can Complete Every Step and Still Fail

One of the most frustrating agent failures is not a crash.

It is successful execution with an unsuccessful outcome.

For example:

Goal:
Find the cause of a checkout failure.
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The agent:

✅ Reads the logs
✅ Searches the repository
✅ Checks the payment API
✅ Finds an error
✅ Modifies the code
✅ Runs tests

Everything appears successful.

But the actual customer problem remains.

Why?

Because the agent investigated a symptom instead of the root cause.

This is one of the major challenges with autonomous systems.

A process can be internally successful while externally wrong.

Anthropic's guidance on evaluating AI agents highlights that agents can call tools, modify state, and adapt over multiple steps, making evaluation more difficult than simply checking whether a single response looks correct. Without structured evaluation, teams can end up discovering failures reactively in production. ([Anthropic][5])

That means developers need to evaluate more than:

Did the agent finish?

They need to ask:

Did the agent achieve the correct outcome?

Those are very different questions.

AI Agents Can Create Memory and Context Bugs

Traditional applications have state.

AI agents do too.

But agent state can include unusual things:

  • Conversation history
  • Previous actions
  • Retrieved documents
  • Tool results
  • Temporary plans
  • User preferences
  • Long-term memory

That creates a new class of bugs.

Imagine an agent working on a software issue.

Earlier in the process, it finds:

The production API uses version 2.
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Later, the agent retrieves an outdated document:

The API uses version 1.
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If the old information becomes more influential than the new information, the agent may make decisions based on outdated context.

Nothing is technically broken.

The model is responding to information it was given.

The real problem is context management.

This is why agent engineering increasingly overlaps with context engineering.

Developers need to manage:

  • What information enters the agent's context
  • What information remains relevant
  • What should be summarized
  • What should be removed
  • Which sources should be trusted

As agents become longer-running systems, context is no longer just an input.

It becomes part of the system's state.

Non-Determinism Makes Reproduction Harder

One of the most valuable debugging techniques in traditional software is reproduction.

A developer might say:

Run these exact steps, and the bug happens.

With AI agents, that can be harder.

The same goal may lead to different:

  • Search queries
  • Tool calls
  • Plans
  • Code changes
  • Intermediate decisions

Anthropic specifically notes that dynamic and non-deterministic behavior makes agent debugging difficult because failures can emerge from many possible decisions in a multi-step process. ([Anthropic][3])

This means teams may need to record more information.

For example:

User Request
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System Instructions
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Agent Version
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Model Version
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Available Tools
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Tool Responses
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Agent Actions
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Final Result
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Without this information, reproducing an agent failure can become extremely difficult.

More Autonomy Also Means More Responsibility

An AI assistant that suggests code is relatively easy to control.

The developer decides whether to use it.

An autonomous agent is different.

It may:

  • Modify files
  • Call APIs
  • Create tickets
  • Send messages
  • Query databases
  • Trigger workflows

The more actions an agent can take, the more careful developers need to be.

Stack Overflow's 2025 survey found that 87% of respondents had concerns about the accuracy of AI agents, while 81% expressed concerns about security and data privacy. ([Stack Overflow Developer Survey][1])

Those concerns are reasonable.

An incorrect chatbot response might waste a few minutes.

An incorrect autonomous action can have a much larger impact.

That is why many agent systems benefit from approval points.

For example:

Agent Investigates
        ↓
Agent Proposes Action
        ↓
Human Reviews High-Risk Change
        ↓
Agent Executes
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The goal is not to eliminate autonomy.

The goal is to apply autonomy where the risk is acceptable.

Developers Are Becoming Agent Debuggers

This may become one of the biggest changes in software engineering.

Developers will still debug:

  • APIs
  • Databases
  • Frontends
  • Infrastructure

But they may increasingly debug:

  • Agent reasoning paths
  • Tool selection
  • Context quality
  • Memory failures
  • Evaluation failures
  • Multi-step workflows

Microsoft Research's study of real developer-agent collaboration found that active iteration and collaboration with agents produced better results than treating the agent as a fully autonomous one-shot system. Developers still had to guide, test, and debug the agent's work. ([Microsoft][2])

This suggests an important shift.

The future workflow may not be:

Developer
   ↓
Writes Code
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It may increasingly look like:

Developer Defines Goal
        ↓
Agent Explores the Problem
        ↓
Developer Reviews Progress
        ↓
Agent Implements Changes
        ↓
Developer Tests the Result
        ↓
Both Iterate
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The developer is not disappearing.

The developer's role is moving.

AI Agents Are Creating an Evaluation Problem

Testing traditional software is already difficult.

Testing an agent can be even harder.

Suppose you build a calculator.

The test is simple:

Input: 2 + 2
Expected Output: 4
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Now consider an AI research agent.

Its task is:

Research the best approach for solving this engineering problem.

What is the correct answer?

There may be multiple valid solutions.

The agent might reach a useful answer through many different paths.

That means teams need new evaluation methods.

They may evaluate:

  • Task success
  • Tool usage
  • Accuracy
  • Safety
  • Cost
  • Response time
  • Number of steps
  • Quality of final output

Anthropic's research on agent evaluations emphasizes that evaluation needs to match the complexity of the agent and that strong evaluations help teams identify behavioral problems before those problems reach users. ([Anthropic][5])

In other words:

If traditional software needs tests, autonomous agents need tests for both results and behavior.

https://goodoff.co/

The Biggest Risk Is Believing the Agent Is More Autonomous Than It Is

AI agents can create an illusion of independence.

You give them a goal.

They begin taking actions.

The system appears to be working.

But apparent autonomy is not the same as reliable autonomy.

This is especially dangerous when an agent performs well on:

  • Demonstrations
  • Simple tasks
  • Familiar workflows
  • Clean data

Production environments are different.

They contain:

  • Missing information
  • Unexpected inputs
  • Legacy systems
  • Failing APIs
  • Conflicting instructions
  • Incomplete documentation

An agent that looks impressive in a demo can still struggle when the environment becomes unpredictable.

That is why engineering teams need to ask a better question than:

Can the agent do this task?

They should ask:

Under what conditions does the agent fail, and can we detect that failure?

That is a much more useful production question.

The Best Agent Systems Will Be Designed for Failure

This may sound pessimistic.

It is actually good engineering.

Reliable systems assume that components can fail.

AI agents should be designed the same way.

A strong agent system should consider:

What if the model chooses the wrong action?

Add validation and approval steps.

What if a tool fails?

Provide error handling and fallback behavior.

What if retrieved information is outdated?

Track sources and prioritize reliable data.

What if the agent becomes stuck?

Set limits on retries and steps.

What if the agent produces an unexpected result?

Capture traces and preserve execution history.

What if the task is too ambiguous?

Allow the agent to request clarification.

The goal is not to build an agent that never makes mistakes.

That is unrealistic.

The goal is to build a system where mistakes are:

  • Detectable
  • Traceable
  • Contained
  • Recoverable

That is classic engineering.

AI Agents Will Probably Change Debugging Before They Eliminate It

AI agents are becoming more capable.

They can save developers time.

They can automate repetitive tasks.

They can explore large codebases.

They can perform multi-step workflows.

The productivity benefits are real. Among developers who use AI agents, Stack Overflow's 2025 survey found strong reports of time savings and productivity gains. ([Stack Overflow Developer Survey][1])

But greater capability creates greater complexity.

The developer may no longer spend all day debugging code written by humans.

Instead, they may spend more time debugging:

  • AI decisions
  • Agent workflows
  • Context
  • Tools
  • State
  • Memory
  • Evaluation systems

That is not necessarily a bad future.

It may be a more productive one.

But it is not a future where engineering disappears.

The Future Developer Will Need to Understand Agent Behavior

The developers who work effectively with AI agents may need a broader skill set.

Writing code will remain important.

But so will:

  • System design
  • Context engineering
  • Observability
  • Evaluation
  • Security
  • Tool integration
  • Workflow design
  • Failure analysis

The question may gradually change from:

How do I implement this function?

To:

How do I design a system that can safely decide when and how to implement this task?

That is a bigger engineering problem.

And bigger engineering problems still need engineers.

Final Thoughts

AI agents are not replacing developers yet.

In fact, their growing complexity is creating new work for developers.

Agents can write code, call tools, retrieve information, and take actions across multiple steps.

But when something goes wrong, developers still need to understand:

  • What the agent knew
  • What it decided
  • Which tools it used
  • What information it received
  • Why it changed direction
  • Why the final result failed

This is the new debugging challenge.

AI agents may reduce the amount of repetitive work developers perform manually.

But they also introduce systems that are more dynamic, less deterministic, and harder to inspect than traditional software.

The future of software development may not be developers versus AI agents.

It may be developers building increasingly capable systems and then learning how to understand, monitor, evaluate, and debug them.

AI agents are not making debugging disappear.

They are giving developers a new kind of software to debug.

And for now, humans are still the ones responsible for figuring out why it broke.

Frequently Asked Questions

What is an AI agent?

An AI agent is a software system that can pursue a goal through multiple steps, often using tools, external data, and intermediate decision-making rather than simply generating a single response.

Are AI agents replacing software developers?

Not yet. AI agents can automate and accelerate some development tasks, but developers are still needed to define requirements, review output, design systems, test behavior, manage security, and debug agent failures.

Why are AI agents difficult to debug?

Agents can make dynamic decisions, use multiple tools, maintain state, and take different paths to solve the same task. A failure may come from the model, context, tool selection, tool output, memory, or the interaction between those components.

What is agent observability?

Agent observability is the ability to inspect how an AI agent behaves, including its actions, tool calls, intermediate steps, execution paths, and outcomes. It helps developers understand why an agent succeeded or failed.

What should developers learn for AI agent development?

Developers working with agents should strengthen skills in system design, context engineering, tool integration, observability, evaluation, security, testing, and failure analysis.

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