Some engineering problems are only painful because they happen so rarely. Even with a coding agent, the frustration still feels the same. I’ll wrestle with a tool that isn't my daily driver, hit a wall of errors, finally find a resolution, and then I neglect to note the solution because the problem is "fixed."
This happened to me recently with a custom, internal GitHub Actions workflow I use for a post-release DevRel task. To make it work, I need to pass a specific authentication token to run Entire in a headless mode.
A couple of months ago, I sat down with my AI agent to configure this for the first time. Because Entire is new and our setup is completely undocumented, it took a grueling trial-and-error process to figure out how to generate the token via a local device-flow login.
Eventually, we found the answer. I pasted the token into my GitHub secrets, the workflow turned green, and I went about my day without writing anything down. I rarely take notes now that I use coding agents, but it’s not a sustainable practice. I need something to take note of the resolution (even if it’s my agent).
Today, when the token expired, I was back at square one.
Why I Didn’t Make a Skill
Normally, my instinct is to automate repetitive tasks by building a reusable workflow, like an Agent skill or a goose recipe. But a dedicated skill didn't make sense here:
- Low Frequency: This happens once every few months. Writing and maintaining code for a skill I barely use is textbook over-engineering.
- Security & Context: Generating an auth token involves sensitive device flows. I didn’t want a generic token-generation script floating around in my global automation suite.
I wished my agent had a memory, so I could ask “How did we get that GENERIC_ENTIRE_TOKEN last time?" and have it recall the context.
Instead, I spent an hour re-debugging a problem I had already solved. It was just my agent and me making guesses.
To break the loop before the next expiration, I decided to use Entire. (At the moment, I was literally working in Entire’s repos. I work for Entire, and I’m like okay this is the perfect use case).
What is Entire?
Entire is a tool for preserving the context behind software work.
When you work with coding agents, a lot of important information lives outside the final diff: the prompt you gave, the dead ends the agent tried, the commands it ran, the reason a change was made, and the little debugging discoveries that never make it into code comments.
Entire captures that session history and connects it to your git history. So instead of only seeing a generic commit message like:
docs: note dispatch auth refresh process
You can also recover the agent session that led to that commit and find the answer to questions like:
- what problem you and the agent solved,
- what files you and the agent touched inspected,
- what scenarios you and the agent ruled out,
- what was the final fix
Using my session history as an artifact
Unfortunately, my agent could remember the solution from two months ago because Entire connects agent sessions to commits.
When I fixed the problem the first time, I hadn't changed any code. I just asked my agent a question and it responded with an answer. This meant I didn’ commit. Because there was no commit, there was no permanent record or session transcript for my agent to review.
So today, after solving the problem a second time, I decided to leave a breadcrumb.
I created a small markdown file in the repo. I didn't want to document the exact terminal commands step-by-step, nor did I want to risk exposing a security vulnerability by pasting sensitive outputs or token patterns into a file. I needed to document the process, not leak the credential.
I checked in a minimalist note that looked like this:
# Workflow Token Refresh
If this workflow fails because its token is missing, blank, or rejected, refresh the the token using the flow captured in the session history for this commit.
Here’s how my agent responded
Now, I wanted to test if this theory would actually work in the future. I asked my agent the following question:
how do i get the GENERIC_ENTIRE_TOKEN?
in my github action workflow it says that token is expired
This triggered my agent to run a skill called using-entire. This skill is an orchestrator built for codebase exploration and tracking down "why did we do this?" questions. Its core instruction is simple: read the repository's recorded session history before guessing from raw code.
Here is the exact step-by-step trace of what the agent did autonomously behind the scenes to find the answer:
- First, the agent ran a quick status check to see if Entire was active in the local project workspace.
entire status
- The agent scanned the codebase for the search keywords from my prompt and successfully located the markdown file I checked in earlier.
docs/runbooks/dispatch-workflow-auth-refresh.md
- Once it found the file, my agent checked the git commit history of that file to find the associated Entire Checkpoint.
git log --format='%H %b' -5 -- docs/runbooks/dispatch-workflow-auth-refresh.md | grep -B1 'Entire-Checkpoint:'
This command allows the agent to extract the unique session identifier tied directly to the commit:
Entire-Checkpoint: f3aaa4d4eafd
This checkpoint hash acts as a permanent anchor, linking the codebase straight back to the exact historical session transcript where we originally solved the problem.
- With the checkpoint ID secured, the agent called Entire's explanation tools to pull the history.
entire explain --checkpoint f3aaa4d4eafd --transcript
- Once the transcript streamed in, the agent scanned the text for relevant authentication phrases like
oauth/device/code, andoauth/token. - Because the agent successfully dug up that historical context, it handed me the exact commands I should run to retrieve the token and add it to my GitHub Actions secrets.
By committing a file to the repo, I forced Entire to generate a permanent session history for that specific commit.
A new approach to institutional knowledge
The first time I used an agent memory tool in mid-2025, it was essentially a manual knowledge graph. It remembered what I explicitly told it to remember, and retrieved it on command.
But I don’t want to manually prompt an agent to remember a scenario. I often lack the hindsight to know what information I will need down the road. This is why Entire’s background recording is so valuable. By automatically saving session transcripts, it transforms raw developer activity into a retrievable artifact that your agent can tap into at any time.
Just a few years ago, every company valued that one engineer who knew everything, holding the context that code alone couldn't capture: the background of architectural decisions, stakeholder trade-offs, and historical workarounds.
We used to have to track that person down for answers. Now, we can query agents instead. But to do that effectively, we must equip them with the right context regarding troubleshooting steps and past failures.
Capturing our agent sessions and version controlling them ensures this rich decision history becomes a live, permanent part of the project repository.
Top comments (1)
The useful memory layer is not just storing more chat history. It is capturing the solved shape of the work: what failed, what fixed it, what command proved it, and when that pattern should not be reused.
That is the kind of knowledge that belongs in a repo-adjacent terminal workflow, because the next agent needs executable context, not a vague recollection.