Executive Summary
SR&ED has always focused on the intellectual parts of technological advancement. In theory, delegating routine implementation to a Large Language Model (LLM) should be a strong catalyst to increase the percentage of your team's eligible SR&ED time. However, the way many teams interact with their AI agents has significantly blurred the line between ideation and implementation, potentially jeopardizing the SR&ED gains they deserve. By improving your documentation process, you can simultaneously boost your efficiency when using coding agents while maximizing your SR&ED returns.
The SR&ED Philosophy
When the Canadian Government established the modern SR&ED program in 1986, they intentionally anchored it around the concept of Knowledge Generation. The program does not require a positive economic return, it doesn't require setting a new industry record, and it doesn't even require that your project succeeds. It merely requires that you attempted to generate new knowledge that contributes to science or engineering.
The government's underlying theory is that the intent to advance the broader Canadian knowledge base is valuable in and of itself. Consequently, the SR&ED program dictates that the actual production of a working system is of secondary importance to the time teams spend systematically thinking about and investigating hard problems in their field.
By this logic, the introduction of LLM coding agents like Claude Code and Cursor should dramatically improve your SR&ED eligibility. They allow your developers and engineers to spend more time brainstorming and designing, and less time doing the fiddly work of boilerplate implementation and configuration.
However, ENTAX has noticed that not all development teams interact with their agents the same way. In some cases, teams are making critical missteps that hurt both their productivity and their SR&ED claims.
The Problem with "Vibe Coding"
When a developer works with an AI agent, they want to ensure the model has the best possible context for what is being built. Often, this means the developer will jump back and forth between high-level ideation (discussing goals, planning, assessing risks, brainstorming solutions) and routine implementation tasks (writing boilerplate, bug fixing, diving through documentation) inside a single chat session.
As more developers do their brainstorming, design, and problem-solving directly within a chat interface, the entire workflow—from conceptualization to having agent swarms build and test code—merges into one continuous thread.
This creates two major issues:
-
It throttles your technical efficiency. You lose the ability to use the most cutting-edge approaches to agentic engineering and strict context management.
-
It dilutes your SR&ED evidence. It mixes the record of important, eligible design decisions with trivial implementation details. More importantly, it makes it nearly impossible for an auditor to isolate the experimental work from routine coding.
The Limits of Context
Agents are limited by their context windows; they can only hold so much information about a task or system architecture at once. Even a massive token context window is not enough to reliably reason about a sprawling, complex codebase. When developers rely on long, unstructured chat sessions, it leads to a series of antipatterns within the AI's outputs:
-
Context Amnesia: The agent forgets the initial architectural constraints or security requirements established earlier in the session.
-
Whack-a-Mole Debugging (Regression Blind Spots): The agent breaks previously working features in an attempt to quickly patch a new, hyper-local bug.
-
Happy-Path Hyperfocus: LLMs are highly optimistic and people-pleasing. Without rigid specifications, they will exclusively code for the "happy path," ignoring edge cases, error handling, and the hidden flaws that arise when things aren't perfect.
Major AI labs know this, which is why tool use has become so critical. Frontier models are often preferred for complex coding tasks not because they have the largest context windows, but because they are adept at intelligently compressing information, discarding irrelevant data, and using search tools (like Grep) on-demand. They gather task-specific information rather than trying to pay attention to an entire chat history at once.
If you rely on one long chat thread, chat histories will rapidly drop out of context, and the memory files created by the agent remain inherently temporary and invisible to your developers.
The SR&ED Reality
By law, SR&ED claims must distinguish between "Experimental Development" (ideation, hypothesizing, experiment design) and the implementation steps (actual computer programming, data collection, routine testing).
Your SR&ED auditor does not care about your finished codebase. They care about your design process. They are looking for evidence that you were experimental in your approach and performed a "systematic investigation or search" to create new IP.
Unfortunately, the CRA will not accept a long, unstructured AI chat log as evidence of a systematic investigation.
The Solution: How to Implement Agentic Engineering
Fortunately, the needs of your AI agents and the requirements of the CRA are perfectly aligned. By shifting from "vibe coding" to structured "Agentic Engineering," you create a workflow that both boosts technical efficiency and satisfies audit requirements: build a detailed design document or project charter that lays out the goals, the problems to solve, and the foreseen risks, then develop a set of specifications and milestones.
This is how AI agents can help you improve your SR&ED claim:
-
Step 1: Document your goals and risks. Create a project charter that tracks your goals, and "why" you made the architectural decisions you have. You should map out the major questions and risks you foresee. When these resources are kept in a centralized document that agents can reference via tool calls, you get a much more resilient product.
-
Step 2: Create precise specifications. Develop detailed design documents that outline how the system is being built, specifying critical constraints, architectural decisions, and milestones.
-
Step 3: Use these as input for your AI agents. Feed these documents into your AI agents (e.g., via RAG or context injection). This ensures your agents code against a strict specification, allowing them to test against edge cases and record their own failures and learnings.
This model keeps your agents focused and reliable while simultaneously generating an audit-ready record of your team's systematic investigations, hypotheses, and experimental results.
Maximize Your Innovation with ENTAX
Navigating the intersection of cutting-edge AI development and SR&ED compliance doesn't have to be a headache. The same rigorous documentation practices that make your LLMs write better code will also safeguard your tax credits.
At ENTAX, we specialize in helping innovative teams align their development workflows with CRA requirements so they never leave money on the table. If you want to ensure your transition to Agentic Engineering is fully optimized for SR&ED eligibility, reach out to the experts at ENTAX today. Let's turn your systematic investigations into maximum returns.