This article explains why current LLMs cannot safely modify real software systems, despite impressive code‑generation demos.
In 2026, the automated software delivery dream is for an agent to:
- read a repository
- understand project structure
- plan a multi‑step change
- write code, tests, and docs
- run the code and fix its own mistakes
- produce a PR‑ready diff
The first three tasks are additive; the last three are transformative. The first three add information without changing the behaviour of the system: they require reading, mapping, and planning, but not altering any existing causal structure in the codebase.
Applying new code is self-contained, additive work; modifying an existing system is transformative work that requires an understanding of dependencies, invariants, and consequences. This distinction — additive vs transformative — is the core reason current LLMs can assist but cannot autonomously deliver software.
Parts of the above can be done but only for tightly controlled demos on simple code that is tens of lines long, not on real-world repositories with thousands of lines of code that has existed for years where dozens of people have updated it.
The agentic work of OpenAI, Google, Cognition Labs, GitHub (Microsoft), Sourcegraph, JetBrains, Replit, Amazon, Meta, and Anthropic, that is listed in Further Reading, was published in 2023 and 2024.
Depending on where you look, you may have been given another impression: that "agents are here". However, reality tells a different story.
Agents are improving, but are not reliable, not autonomous, and not production‑safe.
LLMs can assist with software delivery, but they cannot own it.
LLMs generate statistically plausible continuations of text. This works well for self-contained tasks like writing a function or drafting documentation because these are pattern‑extension problems. But pattern‑matching is not system understanding, and plausibility is not correctness.
Software systems are causal: components depend on each other, invariants constrain behaviour, and changes propagate through the system. The moment a task stops being self‑contained and becomes system‑dependent — requiring dependency coherence, persistent state, or awareness of how changes ripple through a real codebase — pattern‑matching is no longer sufficient.
Currently, LLMs can imitate the shape of engineering work, but they cannot maintain a stable internal representation of a system that must be coherently changed, and that gap is exactly why LLMs fail the moment the task becomes system‑level.
A self‑contained task has no past and no future. A system‑dependent task does.
As soon as a change depends on:
- previous writes
- accumulated data
- cached values
- long‑lived objects
- external system state
any agentic model must reason about how the system got here and how it will behave after the change.
LLMs cannot maintain that internal causal chain.
The gap becomes clear when you compare two activities: writing new code and modifying an existing system.
Code generation is local and additive: the model extends a pattern without needing to understand the system.
But agentic work is global and transformative: the LLM must change the system itself, which requires understanding dependencies, invariants, interactions, and downstream consequences.
This is causal reasoning, not pattern extension. LLMs predict tokens, not consequences — and that is why the leap from writing code to producing a safe, system‑aware PR‑ready diff is not incremental but a shift into a fundamentally different problem space.
A pull request (PR) is a piece of code that will change a system.
For that change to be safe, the change must respect the system's current architecture, its intent, and all downstream consequences.
Software engineers work hard to ensure that such a change is safe through testing and their own judgement and experience before having a collegue review the change.
Applying a change is no longer pattern-matching but understanding causal behaviour: how will the system change if this PR is applied?
The correctness of the PR depends on understanding the whole system, not just generating text.
The LLM must change the system, which requires understanding dependencies, invariants, interactions and consequences, all of which demand causal reasoning, not pattern matching.
Pattern‑matching can write code; only causal reasoning can maintain systems.
Confirm for yourself any claim that you see. Define your own realistic real-world repository to work on, one that is thousands of lines of code, that has supported past real-world work patterns.
Having your own results, applied to your own repository will tell you volumes more than any press release or online anecdote.
For the moment:
- treat agentic AI as a strategic direction
- treat current tools as assistants, not engineers
- invest in clarity, architecture, and test discipline
- expect progress, but not miracles
- do not plan delivery pipelines around unproven capabilities
Maintain human judgement as the centre of the system.
The dream is intact. The evidence is not yet here.
LLM-augmented software delivery does not remove engineering.
It moves engineering up a level.
Humans need to focus on:
- intent
- constraints
- architecture
- correctness
- safety
- trade‑offs
The desired end state is not "AI writes code" but AI maintains systems. If we get there, humans will still need to maintain intent.
The consequence of an agentic system is not to remove engineering, but to elevate it, so that teams spend less time on mechanical construction and more time on judgement, alignment, and shaping the environment in which agents operate.
The organisations that benefit most will be those that treat agentic development not as automation, but as a structural shift in how software is conceived, validated, and maintained.
Until AI can reason causally about systems, human judgement remains the foundation of software delivery.
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OpenAI o1/o3, OpenAI, September, 2024
- https://openai.com/index/introducing-openai-o1-preview/
Gemini Code Demos, Google, December, 2023
- https://blog.google/technology/ai/google-gemini-ai/
Devin, Cognition Labs, March, 2024
- https://www.cognition-labs.com/
GitHub Copilot, GitHub (Microsoft), November, 2023
- https://github.blog/2023-11-08-the-new-github-copilot-your-ai-pair-programmer/
Cody, Sourcegraph, April, 2024
- https://sourcegraph.com/blog/cody-2-0
AI Assistant in JetBrains IDEs, JetBrains, December, 2023
- https://blog.jetbrains.com/blog/2023/12/06/jetbrains-ai-assistant-is-now-available/
Replit Agents, Replit, November, 2023
- https://blog.replit.com/agents
Amazon CodeWhisperer, Amazon, April, 2023
- https://aws.amazon.com/codewhisperer/
Code Llama, Meta, August, 2023
- https://ai.meta.com/blog/code-llama-large-language-model-coding/
Claude 3 Code Reasoning, Anthropic, March, 2024
- https://www.anthropic.com/news/claude-3-family