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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.
NHIMG editorial — based on content published by Ory: The Hidden Cost of Agentic Coding
Questions worth separating out
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 do security and engineering teams get wrong about AI-assisted development?
A: They often confuse faster output with better control.
Practitioner guidance
- Enforce a reuse-before-write gate Require code agents to identify the existing implementation, document where they searched, and explain why the canonical module was not reused before new code is accepted.
- Track the cost of change alongside velocity Add metrics for duplication rate, dead code, and refactor difficulty so delivery dashboards show whether speed is creating future maintenance burden.
- Make cleanup part of the release definition Treat de-duplication, dead-code removal, and test correction as release criteria, not separate technical debt projects that never get scheduled.
What's in the full article
Ory's full blog post covers the operational detail this post intentionally leaves for the source:
- Why limited context windows lead code agents to search poorly in large repositories
- The specific ways agents create duplicate code, dead code, and false test confidence
- Why human engineers still need to own architecture and reuse decisions
- Practical workflow changes for teams that want agentic coding without compounding tech debt
👉 Read Ory's analysis of the hidden cost of agentic coding →
Agentic coding and tech debt: what does it mean for IAM teams?
Explore further
19/08/2026 4:25 pm
Agentic coding debt is a governance problem, not just a productivity trade-off. Ory is describing a familiar failure mode: automation that optimises for immediate output while silently increasing long-term control complexity. The issue is not whether code agents can work, but whether they can preserve a stable architecture without human context. For IAM teams, the parallel is duplicate policy paths that are faster to create than to govern. The practitioner conclusion is simple: measure change quality, not just change volume.
A question worth separating out:
Q: When should human review be mandatory in agentic development?
A: Human review should be mandatory whenever the change affects shared libraries, production paths, or any logic that many systems depend on. Those are the points where context matters most and where a mistaken local optimisation creates the largest long-term cost. Humans bring memory of prior failures that the agent does not have.
👉 Read our full editorial: Agentic coding adds hidden tech debt to production software
