Topic starter
19/08/2026 3:48 pm
TL;DR: Agentic coding can speed delivery by parallelising tasks, but Ory argues that unsupervised use in production code increases duplication, dead code, and long-term maintenance cost because code agents lack durable codebase memory and reuse discipline. The security implication is broader than productivity: when AI systems make local changes without stable context, governance must shift toward controlled reuse, review, and change cost management.
Editorial analysis by NHI Mgmt Group, based on content published by Ory: “The Hidden Cost of Agentic Coding”.
Key questions
Q: How should teams stop AI code agents from creating duplicate logic?
A: Teams should require agents to prove they searched for an existing implementation before writing new code.
Q: Why does agentic coding increase maintenance risk over time?
A: Agentic coding increases maintenance risk because local changes accumulate into parallel implementations, dead code, and inconsistent behaviour.
Q: What are the warning signs that agent-generated code is becoming tech debt?
A: Look for parallel implementations of the same function, unused code paths, tests that repeat the production logic, and repeated changes in the same area after agent-assisted commits.
Practitioner guidance
- Enforce reuse-before-write review Require the agent to identify the existing implementation, explain why it is not sufficient, and cite the files or modules it inspected before generating new code.
- Centralise identity and access logic Keep authentication, authorisation, permissions, and API key handling in shared modules so agents have a clear source of truth instead of inventing parallel flows.
- Flag dead code and duplicate tests in delivery gates Add review checks for unused functions, duplicate paths, and tests that mirror implementation logic instead of validating behaviour.
Bottom line: Agentic coding can improve throughput, but unmanaged reuse decisions turn that speed into hidden technical debt.
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19/08/2026 4:25 pm
Hidden tech debt is the real governance issue in agentic coding: the risk is not simply that AI writes code, but that it writes the wrong kind of code at scale. When agents cannot reliably find the existing implementation, they create parallel logic that is harder to secure, test, and retire later. For IAM and security-sensitive software, that turns reuse discipline into a control objective, not a style preference.
A few things that frame the scale:
- 69% of organisations still authenticate machine identities with long-lived API keys, according to the 2026 State of AI Agent Identity Security Report.
A question worth separating out:
Q: Should teams trust repository memory files to govern agentic coding?
A: No. Memory files and repo rules can help agents stay oriented, but they are static summaries, not a durable understanding of the system. They work best as guardrails for review and search behaviour, while humans remain accountable for architecture choices, control consistency, and high-risk code paths.
👉 Read our full editorial: Agentic coding adds hidden tech debt to production software