Start with one durable project repository and put the key context there in plain text. A single CLAUDE.md file, plus linked notes and decisions, gives you a portable baseline that is easier to back up, review, and share across agents.
Make the repository the durable source of truth
The first move is to reduce workflow fragility by anchoring the work in one repository that survives tool changes, model swaps, and repeated runs. When the key context lives in plain text, teams can inspect it, version it, and restore it without depending on a single agent session or ephemeral prompt state.
A durable repository works because it turns scattered instructions into a recoverable operating base. Keep the notes close to the work, but separate the stable context from transient outputs so the team can tell what should persist, what should change, and what can be regenerated.
If you are choosing what belongs there, prioritise the material that determines how the workflow behaves: task intent, boundary conditions, decision history, and links to the most important supporting notes.
Why plain text beats hidden state at the start
Plain text is resilient because it is easy to diff, back up, search, and audit. AI workflows become brittle when critical assumptions sit only in chat history, tool memory, or agent-specific metadata that cannot be recovered cleanly after an interruption.
The practical advantage is not just readability, it is portability. A simple file can be moved across agents and environments without translation loss, which lowers the chance that a restarted workflow silently changes behaviour.
That also makes review easier. Humans can verify the context directly, rather than infer it from summaries, screenshots, or whatever the model happened to retain from the last turn.
How to make the baseline resilient as the workflow grows
Start small, then harden the baseline with links to related decisions, examples, and operating notes. The goal is not to create a large documentation set, but to make sure the minimum durable context is complete enough that a new agent can continue safely.
- Keep one canonical file for the reusable context.
- Link out to supporting notes instead of duplicating the same material in multiple places.
- Record decisions when they change the workflow, not only when something breaks.
As the workflow expands, the main resilience test is whether another team member or agent can reconstruct the intended behaviour from the repository alone. If they cannot, the baseline is still too implicit.
Risk and Threat Considerations
AI workflows fail most often when context is fragmented, stale, or stored in places that are hard to inspect after an interruption. That creates avoidable exposure to silent drift, because an agent may continue with incomplete assumptions or outdated instructions.
Failure mechanism: Hidden context, duplicated notes, or session-bound memory can cause the workflow to restart with missing state, and that makes it easier for errors to repeat across runs or for bad assumptions to persist unnoticed.
Impact: Teams lose reproducibility, recovery gets slower, and the same mistake can spread across multiple agents or projects before anyone notices the baseline has changed.
Practitioner Guidance
What to prioritise: Establish the smallest durable context set first, then expand only when a repeated decision or failure pattern proves it is needed. If a detail matters for continuity, it belongs in the repository, not only in chat.
What to verify: A fresh agent or teammate should be able to open the repository and answer three questions without extra help, what the workflow is trying to do, what assumptions constrain it, and where the authoritative notes live.
Common mistake: Treating the repository as an archive after the fact. For resilience, it must function as the live baseline that the workflow actually depends on.
Practitioner takeaway: Resilience starts when the workflow can be recovered from durable context alone, not from memory, a single session, or one model’s hidden state.