AI-assisted software development has changed how developers spend their time.
Instead of writing every method manually, developers can ask coding assistants and autonomous agents to:
This can reduce the amount of manual coding required.
But there is another cost that is easier to overlook:
What happens when developers repeatedly accept changes they did not fully understand?
A recent research paper introduced the term Knowledge Debt to describe this problem: developers can gradually accumulate changes that an AI coding agent implemented but that they cannot fully explain or reason about themselves. The paper argues that incidental learning, which traditionally happened while developers struggled through implementation and debugging, can be reduced when that work is delegated to agents.
This is different from traditional technical debt.
Technical debt primarily describes problems accumulated in a software system.
Knowledge debt describes a gap accumulated in the people responsible for understanding and maintaining that system.
The two can eventually reinforce each other.
What Is Knowledge Debt?
Consider a developer working on an unfamiliar service.
Without AI assistance, the workflow might look like:
Requirement
|
v
Read Existing Code
|
v
Investigate Dependencies
|
v
Design Solution
|
v
Implement
|
v
Debug
|
v
Test
During this process, the developer builds mental models.
They learn:
Why the code is structured a certain way
Which components depend on each other
Where failures occur
Which constraints exist
Which design decisions are intentional
With an autonomous coding agent, the workflow can become:
Requirement
|
v
Agent
|
v
Implementation
|
v
Pull Request
The result may be correct.
But the developer may have skipped several opportunities to build understanding.
That missing understanding is the foundation of knowledge debt.
Knowledge Debt vs Technical Debt
These concepts are related but not identical.
| Dimension | Technical Debt | Knowledge Debt |
|---|
| Primary asset affected | Code/system | Developer/team understanding |
| Typical cause | Short-term implementation trade-offs | Excessive delegation without comprehension |
| Main symptom | Hard-to-maintain software | Hard-to-understand software |
| Detection | Static analysis, architecture review | Comprehension checks, review quality |
| Risk | Maintenance cost | Maintenance and decision-making risk |
| Typical remedy | Refactoring | Learning, documentation, deliberate review |
| Can accumulate silently? | Yes | Yes |
A project can have low technical debt but high knowledge debt.
For example:
Code Quality: Good
Tests: Good
Architecture: Good
Developer Knowledge: Low
The system may work perfectly today.
The problem appears when something unusual happens.
Why AI Agents Can Increase the Risk
AI coding agents can perform increasingly large tasks autonomously.
A 2026 longitudinal study of coding-agent adoption found that agent-generated pull requests can produce substantial early velocity gains, while quality risks such as static-analysis warnings and cognitive complexity can persist.
Separately, research on professional developers reports a shift away from direct code creation toward verification and supervisory engineering activities when AI coding assistants are used.
This creates a new engineering requirement:
Developers need enough understanding to supervise the system effectively.
The goal is therefore not to eliminate AI assistance.
The goal is to prevent:
More AI delegation
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v
Less developer understanding
|
v
Weaker review
|
v
More accumulated knowledge debt
Knowledge Debt Has Different Levels
Not every AI-generated line needs the same level of understanding.
A useful model is to divide changes into three categories.
Level 1: Mechanical Changes
Examples:
Rename variable
Format code
Generate boilerplate
Create simple DTO
Update repetitive mappings
Developers generally do not need to deeply investigate every generated character.
Level 2: Behavioral Changes
Examples:
Authentication logic
Caching
Database queries
Business rules
Error handling
API changes
The developer should understand the implementation and its consequences.
Level 3: High-Risk Changes
Examples:
Authorization
Payment processing
Cryptography
Data deletion
Infrastructure
Production deployment
Security boundaries
Concurrency
These require much stronger human verification.
This leads to a practical rule:
The amount of required human understanding should increase with the impact of the change.
The Comprehension Threshold
Instead of asking:
Did the developer read the AI-generated code?
ask:
Can the developer explain why the code is correct?
For example, suppose an AI agent generates:
public async Task<User?> GetUserAsync(
int id,
CancellationToken cancellationToken)
{
return await _context.Users
.AsNoTracking()
.FirstOrDefaultAsync(
user => user.Id == id,
cancellationToken);
}
A developer should be able to explain:
Why AsNoTracking() is appropriate
Why cancellation is passed
What happens when the user does not exist
Whether id requires authorization
What database query is generated
Whether the endpoint exposes sensitive information
Syntax familiarity is not the same as comprehension.
Use AI to Explain Before Accepting
One simple technique is to make the agent explain important changes.
For example:
Explain:
1. What changed?
2. Why was this design selected?
3. What assumptions does it make?
4. What could fail?
5. What tests validate it?
6. Which existing components depend on this behavior?
The explanation should not replace developer review.
It is a mechanism for exposing reasoning that the developer can challenge.
Ask for Alternatives
Knowledge grows when developers compare alternatives.
Instead of:
Implement caching for this service.
ask:
Propose three caching approaches.
For each one explain:
- Consistency implications
- Failure modes
- Memory implications
- Operational complexity
- When it should be used
Then select the appropriate approach.
This preserves an important part of engineering work:
Problem
|
v
Alternatives
|
v
Trade-offs
|
v
Decision
rather than:
Problem
|
v
Generated Solution
Make Architectural Decisions Explicit
Architecture knowledge is especially vulnerable to disappearing into AI-generated code.
Suppose an agent changes:
Controller
|
v
Service
|
v
Repository
to:
Controller
|
v
Service
|
+---- Database
|
+---- Cache
The code may be valid.
But why was the cache introduced?
Record the decision.
A lightweight architecture decision record can contain:
Decision:
Introduce distributed caching.
Reason:
Reduce repeated reads for frequently requested data.
Trade-off:
Potential stale data.
Invalidation:
Explicit invalidation after updates.
Owner:
Platform Team
This converts implicit knowledge into durable project knowledge.
Use Pull Requests as Learning Checkpoints
A pull request should not only answer:
Does the code work?
It should also help answer:
Do the reviewers understand the change?
For AI-assisted changes, add review questions such as:
What did the agent change?
Why was this approach selected?
Which assumptions were made?
Which edge cases were tested?
Which existing behavior could change?
What remains uncertain?
This is especially valuable for large agent-generated pull requests.
Avoid Huge Agent-Generated Pull Requests
A large change is harder to understand regardless of who wrote it.
Consider:
Bad:
1 Agent Task
|
v
8,000 lines changed
versus:
Better:
Task 1 -> Data Model
Task 2 -> Service
Task 3 -> API
Task 4 -> Tests
Smaller changes improve:
Reviewability
Debugging
Rollback
Knowledge transfer
Code ownership
The goal is not to restrict agents unnecessarily.
It is to keep the human verification surface manageable.
Establish a Change Budget
A team can define limits for autonomous changes.
For example:
Low Risk:
Agent can create PR automatically.
Medium Risk:
Agent can create PR; human review required.
High Risk:
Agent can propose changes; human approval required before execution.
This creates a risk-based workflow.
A similar principle applies to production systems:
Low Impact
|
v
More Automation
High Impact
|
v
More Human Verification
Require Tests for Behavioral Changes
AI-generated code should not be considered understood merely because it compiles.
For a behavioral change, require tests.
Example:
[Fact]
public async Task Returns_NotFound_When_User_Does_Not_Exist()
{
var result =
await service.GetUserAsync(999);
Assert.Null(result);
}
The test documents expected behavior.
It also provides a learning checkpoint for the developer reviewing the generated implementation.
Do Not Let AI Generate Both Code and Proof Unchecked
A dangerous workflow is:
Agent writes code
|
v
Agent writes tests
|
v
Tests pass
|
v
Merge
The tests may encode the same incorrect assumptions as the implementation.
A stronger workflow is:
Developer defines behavior
|
v
Agent implements
|
v
Agent proposes tests
|
v
Developer reviews behavior
|
v
Independent verification
The developer remains responsible for the expected behavior.
Use Test-First AI Workflows
One practical technique is to ask the agent to describe the tests before implementing the feature.
For example:
Before modifying the code:
1. Identify the expected behavior.
2. List the edge cases.
3. Propose the tests.
4. Explain which existing tests may be affected.
5. Wait for approval.
Then implement.
This introduces deliberate reasoning before code generation.
Preserve the Debugging Experience
Debugging is one of the ways developers develop strong mental models.
If an agent immediately fixes every failure, developers can miss the diagnostic process.
Instead of:
Fix this error.
occasionally use:
Analyze this failure.
Explain:
- likely root causes
- evidence supporting each cause
- what diagnostic would distinguish them
- recommended fix
This turns the AI into a reasoning assistant rather than a replacement for diagnosis.
Use Learning Mode for Important Changes
A useful development workflow can have two modes.
Fast Mode
Use for:
Boilerplate
Formatting
Simple refactoring
Repetitive code
Documentation
The agent optimizes for speed.
Learning Mode
Use for:
Architecture
Concurrency
Security
Performance
Database behavior
Distributed systems
Complex algorithms
The agent must explain:
Problem
Approach
Alternatives
Trade-offs
Failure Modes
Tests
This keeps learning effort proportional to engineering importance.
Ask the Agent to Identify Unknowns
One useful prompt pattern is:
Before implementing this change, list the assumptions
you are making about the existing system.
For each assumption:
- Explain why you believe it is true.
- Identify what evidence could verify it.
- State what could happen if it is wrong.
This helps expose hidden dependencies.
It also creates opportunities for the developer to correct the agent.
Keep Humans in the Architecture Loop
AI agents are particularly good at producing implementations.
Architecture still requires broader context.
For example:
Agent knows:
Code
Tests
Configuration
Documentation
Developer may also know:
Business constraints
Historical decisions
Team ownership
Operational limitations
Compliance requirements
Upcoming migrations
That organizational context may not exist in the repository.
Therefore:
Do not delegate architectural decisions simply because the agent can generate architectural code.
Maintain a Decision Log
A lightweight decision log can prevent knowledge from disappearing.
For example:
# Architecture Decision
## Decision
Use asynchronous messaging for order processing.
## Reason
Order processing can tolerate eventual consistency.
## Rejected Alternative
Synchronous HTTP call.
## Trade-off
Higher operational complexity.
## Consequence
Order status becomes eventually consistent.
The document becomes useful to:
Developers
Reviewers
New team members
AI agents
Future maintainers
Give Agents Access to Project Context
Knowledge debt can also occur because the agent itself lacks context.
A strong coding-agent environment should provide:
Architecture Documentation
Coding Standards
API Contracts
ADR Files
Testing Guidelines
Security Rules
Build Instructions
Deployment Documentation
This reduces the probability that the agent invents its own assumptions.
Research on collaborative software-engineering agents similarly emphasizes adherence to standards and processes, code quality, problem solving, and collaboration with developers as important agent behaviors.
Treat Documentation as an Engineering Dependency
Documentation should not be treated as something written after the code is finished.
For important changes:
Code Change
|
+---- Tests
|
+---- Documentation
|
+---- Architecture Decision
This gives future developers enough context to understand why the code exists.
Build a Knowledge Map
For complex systems, create a lightweight map:
Orders
|
+-- OrderService
|
+-- PaymentService
|
+-- InventoryService
|
+-- Message Queue
For each component, document:
Purpose
Dependencies
Inputs
Outputs
Failure Modes
Owner
Important Constraints
This is useful for humans and AI agents.
Use Code Ownership Carefully
Knowledge debt becomes particularly dangerous when only one person understands a subsystem.
Avoid:
AI-generated subsystem
|
v
One developer understands it
Prefer:
Subsystem
|
+---- Owner A
+---- Reviewer B
+---- Documentation
+---- Tests
The objective is to avoid a single point of knowledge failure.
Rotate Review Responsibilities
If the same developer always reviews AI-generated code, the team's collective understanding may remain uneven.
Rotate reviewers for important components.
For example:
Week 1:
Developer A -> Service
Week 2:
Developer B -> Service
Week 3:
Developer C -> Service
This can increase shared system knowledge.
Use Pairing With AI, Not Blind Delegation
There is a meaningful difference between:
Human asks
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v
Agent executes everything
and:
Human defines problem
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v
Agent proposes solution
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v
Human evaluates
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v
Agent implements
|
v
Human verifies
The second workflow preserves more opportunities for engineering judgment.
Measure Knowledge Debt Indirectly
Knowledge debt is difficult to measure directly.
Do not invent a precise "knowledge debt score" without a validated methodology.
Instead, use observable signals.
For example:
| Signal | Possible Interpretation |
|---|
| Reviewer cannot explain change | Knowledge gap |
| PR requires repeated clarification | Context gap |
| Developer struggles to modify generated code | Comprehension gap |
| Same area repeatedly regenerated | Ownership gap |
| Architecture decisions undocumented | Decision knowledge gap |
| Large agent-generated PRs | Reviewability risk |
| Increased complexity | Potential technical debt |
These are indicators, not definitive measurements.
Use Comprehension Checks
For high-impact changes, reviewers can answer:
1. What does this code do?
2. Why is it implemented this way?
3. What assumptions does it make?
4. What happens when the dependency fails?
5. What happens under concurrency?
6. What security boundary does it cross?
7. Which tests prove the behavior?
If the reviewer cannot answer these questions, the change may need more investigation.
Introduce Deliberate Friction
Automation is valuable because it removes friction.
But removing every form of friction is not necessarily beneficial for learning.
The 2026 "Agents That Teach" research argues that incidental learning should be intentionally designed back into AI-assisted development rather than assumed to happen automatically.
Useful friction includes:
Explain
Compare
Review
Test
Debug
Document
The goal is not to make developers slower.
It is to preserve the parts of development that create durable expertise.
Teach Through Exceptions
A practical strategy is to let AI automate routine work while deliberately preserving difficult reasoning tasks.
For example:
Automate:
DTO generation
Mapping
Boilerplate tests
Formatting
Human focuses on:
Architecture
Failure analysis
Security
Performance
Trade-offs
This can make AI a multiplier rather than a replacement for engineering judgment.
Use AI to Generate Learning Questions
Instead of asking only:
Explain this code.
ask:
Generate five questions a senior developer
should be able to answer before approving this change.
For example:
1. Why is this transaction boundary here?
2. What happens if the downstream API times out?
3. Can two requests update this record concurrently?
4. What prevents unauthorized access?
5. Which index supports this query?
The questions themselves can reveal areas requiring investigation.
Keep an AI Change Summary
Every substantial agent-generated pull request can include:
## AI-Assisted Change Summary
### What Changed
...
### Why
...
### Important Decisions
...
### Assumptions
...
### Tests
...
### Risks
...
### Human Review
...
This does not replace code review.
It makes the review process more structured.
Track Provenance
For larger organizations, record whether a change was:
Human-authored
AI-assisted
Agent-generated
AI-refactored
Research on coding-agent adoption recommends provenance tracking and quality safeguards because agent-generated changes can affect code quality over time.
Provenance does not mean blaming AI.
It means making the development process observable.
Do Not Treat AI Attribution as a Quality Score
An AI-generated change is not automatically bad.
A human-written change is not automatically good.
The useful question is:
Was the change understood,
reviewed, tested, and maintained?
rather than:
Was AI involved?
Knowledge Debt and Junior Developers
The risk can be particularly important for developers early in their careers.
Traditional development exposes beginners to:
Syntax
Debugging
Documentation
Design
Failure
Testing
Code Review
If every difficult task is immediately delegated, some of those learning opportunities can disappear.
A 2026 paper on AI-assisted development explicitly describes this as the loss of incidental learning and argues that learning-aware AI systems should intentionally reintroduce educational moments.
This does not mean junior developers should be prohibited from using AI.
Instead:
AI writes
+
Junior explains
+
Senior reviews
can be more educational than:
AI writes
+
Junior approves
Knowledge Debt and Senior Developers
Senior developers are not immune.
A senior engineer may understand a system well today but gradually become less familiar with areas they rarely touch because an agent handles them.
A recent longitudinal study reported that developers' work can shift toward verification and supervisory activities as AI assistance increases.
This means senior engineers need deliberate mechanisms for maintaining deep system knowledge too.
Use Periodic Manual Implementation
For selected learning-critical areas, developers can occasionally implement small components manually.
Examples:
Write a query manually
Implement a small algorithm
Debug without immediate AI intervention
Design a component before asking AI
Review generated code without explanation first
The purpose is not nostalgia for manual coding.
The purpose is maintaining the ability to reason independently when AI output is wrong.
A Practical Team Policy
A balanced AI-assisted development policy could look like this:
| Change Type | AI Use | Human Requirement |
|---|
| Formatting | Allowed | Basic review |
| Boilerplate | Allowed | Review |
| Simple refactor | Allowed | Tests + review |
| Business logic | Allowed | Explain + tests |
| Database changes | Allowed | Query review + tests |
| Security code | Restricted | Specialist review |
| Production deployment | Controlled | Explicit approval |
| Data deletion | Controlled | Human authorization |
| Architecture | Advisory | Human decision |
The exact policy should be adapted to the organization's risk profile.
A Practical Developer Workflow
A useful workflow is:
Step 1: Understand the Problem
Before asking the agent to implement anything, define:
Goal
Constraints
Expected Behavior
Non-Goals
Step 2: Ask the Agent to Investigate
Let the agent inspect:
Repository
Dependencies
Tests
Architecture
Configuration
but ask it to report findings before making large changes.
Step 3: Review the Proposed Design
Ask for:
Approach
Alternatives
Trade-offs
Risks
Step 4: Implement in Small Changes
Keep the change set manageable.
Step 5: Review the Code
Do not approve solely because tests pass.
Step 6: Run Tests and Diagnostics
Verify:
Unit Tests
Integration Tests
Static Analysis
Security Checks
Performance Tests
where applicable.
Step 7: Record Important Knowledge
Update:
Documentation
ADRs
Runbooks
API Contracts
Step 8: Teach the Team
For important changes, share the reasoning during code review or technical discussion.
Common Mistakes
Letting AI Own the Design
AI can propose architectures, but important architectural decisions should remain accountable to humans.
Reviewing Only the Diff
A diff shows what changed.
It does not necessarily explain why.
Trusting Passing Tests
Tests can verify only the behaviors they cover.
Accepting Huge PRs
Large AI-generated changes are difficult to comprehend and review.
Never Debugging Manually
Developers can lose diagnostic intuition if every failure is immediately delegated.
Asking AI for the Answer Too Early
Sometimes developers should investigate the problem before requesting a solution.
Ignoring Documentation
Undocumented decisions become future knowledge debt.
Assuming Senior Developers Are Immune
Knowledge decay can affect experienced engineers too.
Measuring Productivity Only by Lines of Code
AI changes the economics of code production.
More generated code does not necessarily mean more maintainable software.
Troubleshooting Knowledge Debt
The Team Cannot Explain an Existing Module
Stop adding features blindly.
Create a knowledge-recovery task:
Map module
Document dependencies
Add tests
Trace critical workflows
Record architecture decisions
Developers Keep Asking AI to Explain Existing Code
Treat this as a signal.
The codebase may lack:
Documentation
Tests
Architecture diagrams
Ownership
Decision records
AI Keeps Rewriting the Same Area
Investigate why.
Possible causes include:
Poor documentation
Unclear requirements
Missing tests
Inconsistent architecture
Agent lacks repository context
Reviews Are Becoming Superficial
Reduce PR size.
Introduce review checklists.
Assign reviewers with relevant domain knowledge.
Require explanations for high-risk changes.
New Developers Cannot Work Without AI
Introduce learning-oriented tasks.
Ask developers to first propose an approach, then use AI to validate or accelerate it.
The objective is not to remove AI.
It is to prevent dependency from becoming the only way the developer can solve the problem.
Best Practices
Use AI to accelerate implementation, not eliminate understanding.
Match human review depth to the risk of the change.
Keep agent-generated changes small and reviewable.
Ask for alternatives before accepting architectural solutions.
Require explanations for important behavioral changes.
Use tests as behavioral documentation.
Record important architecture decisions.
Preserve debugging and diagnostic skills.
Give AI agents access to authoritative project context.
Track provenance for significant agent-generated changes.
Rotate reviewers to distribute system knowledge.
Use deliberate learning checkpoints for complex work.
Do not treat passing tests as proof of comprehension.
Do not measure AI productivity solely by generated code volume.
Protect security-sensitive and high-impact operations with stronger human controls.
Review generated code for maintainability and complexity.
Periodically audit areas heavily maintained by AI agents.
Create documentation while the architectural reasoning is still fresh.
Use AI explanations as discussion material, not unquestionable authority.
Design development workflows where productivity and learning reinforce each other.
Frequently Asked Questions
Is knowledge debt the same as technical debt?
No.
Technical debt primarily concerns compromises or maintenance costs in the software itself.
Knowledge debt concerns the loss or absence of developer understanding needed to maintain and evolve that software.
They can interact:
Low Understanding
|
v
Weak Review
|
v
Poor Decisions
|
v
Technical Debt
Does using AI automatically create knowledge debt?
No.
AI assistance can accelerate development without creating significant knowledge debt when developers understand important changes, maintain documentation, review architecture, and deliberately preserve learning opportunities.
The risk comes from unexamined delegation, not AI usage itself.
Should developers stop using AI to prevent knowledge debt?
No.
That would unnecessarily sacrifice useful productivity improvements.
A better approach is:
Automate Routine Work
+
Preserve Human Reasoning
+
Review Important Changes
+
Document Decisions
Should junior developers use AI coding agents?
They can, but the workflow should emphasize learning.
For example:
Junior proposes approach
|
v
AI provides alternatives
|
v
Junior implements/reviews
|
v
Senior reviews important decisions
This provides both productivity and learning.
How can I detect knowledge debt?
There is currently no universally accepted production metric that can directly measure an individual team's knowledge debt.
Instead, monitor signals such as:
Developers cannot explain recent changes.
Large generated PRs are difficult to review.
Engineers repeatedly ask agents to explain their own code.
The team depends heavily on one person for system knowledge.
Developers struggle to modify AI-generated components.
Architecture decisions are undocumented.
These should be treated as warning signals rather than definitive measurements.
Should every line of AI-generated code be understood?
Not necessarily.
The required level of understanding should depend on risk.
A generated property mapping does not require the same review depth as:
Authentication
Authorization
Payments
Concurrency
Cryptography
Data deletion
Infrastructure
The goal is meaningful comprehension of important behavior, not manual inspection of every character.
Can documentation eliminate knowledge debt?
No.
Documentation helps preserve explicit knowledge, but developers still need enough hands-on understanding to reason about changing systems.
Documentation and practical experience should complement each other.
Should AI-generated code require special code review?
Not necessarily because it was generated by AI.
However, code that introduces substantial behavioral, architectural, security, or operational changes deserves appropriate review regardless of its origin.
AI provenance can provide useful context, but quality should remain the primary criterion.
Can AI help prevent knowledge debt?
Yes.
AI can actively support learning by:
Explaining design decisions
Generating review questions
Identifying assumptions
Comparing alternatives
Creating documentation
Explaining failures
Generating test cases
The recent "Agents That Teach" research specifically argues for designing AI systems that intentionally create learning opportunities instead of assuming incidental learning will continue automatically.
Conclusion
AI-assisted development is changing the role of the software engineer.
The shift is increasingly moving from:
Write Every Line
toward:
Define
Direct
Evaluate
Verify
Maintain
Recent research suggests that this transition can produce meaningful productivity benefits while also creating new concerns around code quality, developer experience, and the loss of incidental learning.
The concept of knowledge debt captures one particularly important risk:
AI Performs More Work
|
v
Developer Practices Less Direct Reasoning
|
v
Understanding Grows More Slowly
|
v
Knowledge Debt Accumulates
The answer is not to reject AI.
The answer is to design better workflows.
A sustainable AI-assisted engineering process should look more like:
Human Defines Problem
|
v
AI Investigates
|
v
Human Reviews Approach
|
v
AI Implements
|
v
Human Verifies
|
v
Tests Validate Behavior
|
v
Documentation Preserves Knowledge
This keeps AI responsible for what it does well while preserving human ownership of engineering judgment.
The most important principle is simple:
Optimize for developer leverage, not developer replacement.
The strongest AI-assisted teams will not necessarily be the teams where agents write the most code.
They will be the teams where developers can use agents aggressively while still understanding the systems they are responsible for building, securing, debugging, and maintaining.