Technical debt can exist in conventional software, machine learning, and agentic systems. These categories overlap.

The engineering concern predates today’s agents. Sculley and colleagues’ 2015 research on hidden technical debt in machine learning systems described risks involving data dependencies, configuration, hidden feedback loops, and interconnected components.

Agents add an important production consideration: model output can influence what happens next in an external system.

Anthropic distinguishes predefined workflows from agents that dynamically direct their processes and tool use. Both can accumulate debt, but dynamic action selection adds decisions that need to be bounded and tested.

That is why AI agent technical debt cannot be assessed through code quality alone. A clean codebase may still contain unclear tool semantics, unreliable success checks, or a workflow nobody can safely resume after an interruption.

This is also different from debt in AI-generated code. Code written with an AI assistant can have ordinary software defects or maintenance problems even when the deployed product contains no agent. Here, the focus is the agent system operating in production.

Production agents need evaluation, monitoring, maintenance, and sometimes human review. Those activities do not automatically indicate poor engineering.

A deliberately chosen approval step may be essential. Reviewing a new model before deployment may be sensible maintenance. A fallback can be a well-designed control.

Debt appears when an unresolved gap repeatedly creates avoidable work or makes future change harder.

Recent agentic-debt research also separates accumulated design liability from recurring operating burden. A well-engineered agent still has operating costs. Debt can increase those costs, but an invoice or escalation count alone does not establish the cause.

For technical debt in AI systems, the practical assessment is the gap, its consequences, and whether a specific repair can reduce them.

The following categories are a practical review structure for this article. They are not an industry certification or a validated scoring system.

1. Instructions that carry too much business logic

Early prompts often acquire exceptions one incident at a time. Eventually, eligibility rules, routing instructions, tool advice, and tone requirements compete in the same text.

One amendment may solve a visible failure while making another behavior less predictable.

What to examine: Which instructions guide judgment, and which specify conditions the application must always enforce?

Use the prompt for interpretation and task guidance. Enforce authorization, required approvals, and other hard constraints in application or tool code. Test the interaction between those controls and the model.

For example, a customer-service agent can interpret a request and propose a resolution while server-side logic checks whether that user can perform the action.

This recommendation is consistent with OWASP’s guidance on limiting functionality, permissions, and autonomy. A natural-language instruction is not a substitute for an enforced permission boundary.

2. Tools whose meaning is unclear

An API that works for application developers is not automatically a well-designed agent tool.

Suppose a tool called update_customer accepts free text, changes several fields, and returns only success. The agent and the incident responder have little information about what the operation actually did.

Specify the allowed inputs, target identifiers, preconditions, effects, error categories, and returned evidence. Distinguish read operations from operations that change records or send communications.

Anthropic’s tool-design guidance emphasizes clear tool purposes, useful responses, and evaluation against realistic tasks.

Debt signal: The team keeps rewriting prompts to compensate for overlapping tools or ambiguous responses.

Repair evidence: The tool rejects invalid inputs, reports meaningful failures, and returns enough information to check the intended result. Changing a tool description or schema should trigger relevant behavioral tests.

3. Context and memory without clear boundaries

An agent may receive instructions, conversation history, retrieved documents, tool responses, and stored memory. These sources can differ in freshness and trustworthiness.

A larger context window does not establish that the right information has been supplied. Anthropic’s context-engineering guidance treats context as a resource that needs deliberate selection and management.

Define the source, scope, retention, and update rules for important information. Separate temporary task state from longer-lived memory. Keep tenant and user access boundaries explicit.

Illustrative failure: An agent retrieves an outdated policy and applies it to a new request, but the trace does not record the policy version.

The repair could include source metadata, freshness checks, and evaluation cases involving conflicting or outdated documents. Adding another paragraph to the prompt would leave the information-management gap unresolved.

OWASP’s agentic security work also identifies memory and context poisoning as a distinct risk. Persistent information needs controls as well as storage.

4. Orchestration without explicit state and limits

Agent orchestration debt accumulates when routing, delegation, handoffs, retries, and stopping conditions become difficult to inspect or change.

A planner may delegate to several workers, but who decides whether the overall task is complete? What happens if one worker times out after another has updated a record?

Make workflow states, handoff data, responsible components, and termination conditions explicit. Set appropriate limits on execution time, steps, and resource use. Understand which layer owns retries.

AWS recommends bounded retries with backoff and jitter, including testing retry behavior rather than assuming defaults are suitable. In an agent workflow, examine both service retries and higher-level attempts to repeat the task.

Checkpointing can preserve progress. LangGraph’s persistence documentation distinguishes thread checkpoints from data stored across threads. Stored progress still needs clearly defined resume behavior and an appropriate retention policy.

5. External actions without recovery semantics

A tool request can time out after a downstream system has accepted it. Repeating the call may create another action unless the system recognizes it as the same operation.

AWS’s guidance on idempotent APIs explains how caller-supplied request identifiers can support safe retries.

For an agent, preserve the identity of the intended business operation across retries. Also define what happens when the same identifier is supplied with conflicting parameters or arrives after the relevant retention window.

Idempotency does not prove correctness. A system can perform the wrong action exactly once.

Record the action, its authorization, its result, and how to reconcile an uncertain outcome. Some actions need a business-specific compensating operation or manual intervention. Microsoft’s compensating-transaction guidance makes clear that recovery is application-specific and may itself fail.

These are common AI agent architecture problems because workflow state and external business state are related, but they are not interchangeable.

Reverting a deployment does not automatically undo record changes or communications the previous version has already made.

6. Evaluations that check the answer but miss the outcome

An agent saying that a task is complete is evidence of what it reported. The actual system state establishes whether the intended change occurred.

Anthropic’s guidance on agent evaluations distinguishes the interaction trace from the final outcome and recommends repeated trials because behavior can vary.

Build tests around representative tasks, permissions, failure conditions, and acceptable outcomes. Check observable state when the agent changes something. Use human-calibrated rubrics for qualities that cannot be established through simple assertions.

Illustrative failure: An address-change agent sends a confirmation message even though the customer record still contains the old address.

The test should check the intended record and required authorization, not just whether the response sounds correct.

Where several execution paths are valid, allow them. Require specific steps when they enforce a necessary control, such as approval or identity verification. Keep test environments isolated, and turn production failures into regression cases.

Choose release criteria according to the task and consequences of failure. A general success percentage can conceal an unacceptable result on a particular high-impact action.

7. Releases and operations without an accountable owner

Model configuration, prompts, tools, retrieval settings, workflow code, and policies can change independently. Without a release record, identifying the configuration associated with a failure becomes harder.

Record the relevant versions or identifiers and test the combination you intend to release. Treat provider-controlled changes as dependencies to monitor, rather than assuming every model behavior can be permanently pinned.

For AI agent maintenance, assign responsibility for evaluations, tool compatibility, context freshness, access controls, incidents, and business outcomes. A small team can assign several responsibilities to one person, but the responsibilities should remain visible.

Capture observable execution evidence: tool calls, operation identifiers, relevant configuration, state transitions, errors, timing, resource use, and verified outcomes. Apply access and retention controls to sensitive trace data.

Google Cloud’s current agent-observability guidance covers model interactions, tool usage, execution paths, latency, safety, and quality. An uptime dashboard alone cannot establish whether the agent completed the business task correctly.

Consider a hypothetical SaaS support agent handling subscription-plan changes. It reads the account, checks eligibility, submits the change, updates the support case, and sends a confirmation.

During one attempt, the plan-change request times out. The application does not know whether the change was accepted. It repeats the request, then closes the support case using the agent’s final message as the completion signal.

The engineering gaps are specific:

• Operation identity is not reliably preserved across retries.
• The uncertain downstream outcome has no reconciliation path.
• Case closure does not require evidence of the intended plan state.

Adding another agent to inspect the message would not, by itself, close those gaps. The team needs to improve the action boundary, completion check, and recovery path.

Now consider an illustrative monthly workload. These figures are invented for the calculation and are not Devine Globe results or industry benchmarks.

Assume 1,000 requests were processed and 900 met the predefined criteria for successful completion, including any required human assistance.

The direct usage bill is $0.22 per attempted request. The wider operating cost is approximately $1.53 per verified successful completion:

$1,380 ÷ 900 = $1.53, rounded to the nearest cent.

These are different measurements with different denominators. The usage bill is accurate, but it is incomplete as a measure of delivered value.

The $1,380 is also not a technical-debt estimate. Some review and maintenance would remain in a sound design. Establish the avoidable portion by linking additional work to specific gaps, repairing them, and measuring the change.

For a defined task class and measurement period, use this planning formula:

Cost per verified successful outcome = attributable model, tool, runtime, review, recovery, and maintenance costs ÷ verified successful outcomes.

This is a suggested management calculation, not an industry-standard metric.

Keep the workload, quality requirements, labor assumptions, and allocation method consistent when comparing periods or approaches. Allocate shared costs once, and identify any costs excluded from the calculation.

Report autonomous completions separately from human-assisted completions. Also track valid refusals and appropriate escalations: an agent should not be rewarded for forcing completion when the correct behavior is to stop.

Pair cost with operational evidence:

None of these metrics independently proves debt. A changing workload, provider outage, or deliberately stricter policy can change the results. Investigate the cause before calling the movement a design problem.

A useful register describes the missing decision or control, rather than recording that the agent is unreliable.

Estimate repair effort separately from recurring impact. Where evidence permits, translate avoidable labor into a comparable cost. Record uncertainty instead of filling missing data with confident guesses.

Prioritize harmful or unauthorized action paths first. Then address problems that prevent reliable verification and recovery. After those, compare recurring operational waste and release friction with the effort needed to reduce them.

When postponing a repair, name the owner, temporary control, and review trigger. Deliberately accepted debt should remain inspectable.

Start with one important workflow and its most consequential failure paths.

1. Define the outcome. Agree what successful completion, valid refusal, and escalation mean for that task.
2. Map the action boundaries. Identify which operations read information, change state, or create hard-to-reverse effects.
3. Constrain authority. Enforce permissions, approval rules, and execution limits in the relevant application layers.
4. Test realistic interruptions. Exercise timeouts, malformed results, outdated context, denied access, and interrupted handoffs in a suitable test environment.
5. Make uncertain results recoverable. Establish operation identity, state evidence, reconciliation, and responsible owners.
6. Reassess unnecessary complexity. Compare simpler alternatives where they can satisfy the requirements.
7. Measure the change. Check outcomes, risk, total cost, and repair effort after the intervention.

Anthropic’s agent-design guidance recommends starting with simple approaches and adding complexity when justified. Multi-agent designs can be useful, but each handoff should have a reason and evidence that its benefit outweighs its coordination cost.

Routine AI agent production challenges still need operational attention after these repairs. Debt reduction should make those challenges easier to diagnose and manage; it cannot guarantee error-free agents.

Before extending autonomy or adding another agent, establish what the current workflow is authorized to do, what evidence proves completion, and who resolves an uncertain result.

Agentic technical debt becomes manageable when it is connected to a specific engineering gap, an observable consequence, and an accountable repair. That gives technical teams a clearer maintenance backlog and business leaders a more complete view of delivery cost.

At Devine Globe, our AI agent development, AI integration, and product engineering services can support teams building new capabilities or improving existing software. If your agent workflow is creating repeated exceptions or unclear maintenance responsibilities, share the workflow and the issues you are seeing.