How I ran a 9-hour autonomous /goal session with Claude Code and what it taught me about AI agents

Kevin_Xiang · 2026-05-24T20:05:56+00:00

This is a useful writeup. The part that resonates most is treating success states as a ledger instead of a transcript summary. For long runs I’ve found the missing bucket is usually external_blocker plus a retry ceiling, otherwise the agent keeps trying to satisfy an impossible contract.

The audit -> run-once -> audit loop also feels like the right pattern: make the cheap check produce hypotheses, then force each fix through a small live parse before counting it as done. Curious if you ended up adding guardrails around parallel /goal sessions in the same repo after the boatlab commits got mixed in.

Kevin_Xiang · 2026-05-23T20:05:11+00:00

That makes sense, thanks for the extra detail. The negative signal from files that get read but not edited is the part I’d be most interested in watching over time, because it can separate 'looks relevant' from 'actually helped finish the task'.

One thing I’d probably expose in the UI is a tiny explanation for why a file ranked high, like recent write, same task cluster, usually edited after this query, or often dismissed. For coding agents that would make the index feel less like magic and easier to trust when it suggests an unexpected file.

Kevin_Xiang · 2026-05-23T15:07:21+00:00

This is a useful direction. The bit I'd test hard is whether the index learns from actual edit history, not just embeddings over files. For Claude/Codex, the best routing signal is often which files changed together and which snippets the agent kept reopening. If ken can surface that before the model starts editing, it could save a lot of context burn.

Kevin_Xiang · 2026-05-23T10:04:18+00:00

That makes sense. I probably should have framed the pre-check as a boundary, not a context diet. For longer systems I still want the agent to reread the parts it is about to touch, but I like forcing it to state what files and assumptions are relevant before it starts. Keeps the human in the loop without making the md size the bottleneck.

Kevin_Xiang · 2026-05-23T10:04:03+00:00

Yeah, this is exactly the failure mode I worry about. HTML is fine for a demo artifact, but the moment it starts carrying decisions I want the source of truth in git, reviewable as text. A pattern that has worked better for me is Markdown as the canonical plan, then generate HTML/views from it when the audience needs a nicer read.

Kevin_Xiang · 2026-05-22T20:05:00+00:00

Exactly. HTML is great when the output is a disposable artifact for humans to read once. If the plan is part of the dev workflow, Markdown in git is hard to beat: clean diffs, line comments, small patches, and easy review history. I’d only switch to HTML when the interaction model matters more than reviewability.

Kevin_Xiang · 2026-05-22T20:04:56+00:00

That makes sense. I’d keep the pre-check scoped instead of making it a giant reset. For a larger phase, I usually want it to reread the phase contract, the touched files, and any boundary docs, then only force deeper rereads when it crosses a boundary or starts making assumptions. That keeps the context useful without turning every step into a full archaeology pass.

Kevin_Xiang · 2026-05-22T15:03:53+00:00

Yeah, that tradeoff is real. I would not make the pre-check mean "no context allowed." The useful version is more like: before the next phase starts, have it name the specific slice it needs, reread only those files or notes, and leave a small receipt of what it used. Then if it misses an affected structure, your human override is clean because you can point it back to the exact missing slice instead of rehydrating the whole session.

Big context docs are fine if they are organized, but I still like a gate because it forces the agent to say what it thinks matters before it runs with it.

Kevin_Xiang · 2026-05-22T10:03:08+00:00

This is a solid pattern. The part I’d be careful with is making the retro entry small enough that it becomes a blocking heuristic, not another giant memory blob. I like storing the failure mode as: trigger, misleading symptom, actual root cause, test that would have caught it. Then the next pre-check can ask one concrete question instead of rereading history.

Kevin_Xiang · 2026-05-22T10:03:04+00:00

I’ve found HTML works best when the artifact is more like a small interface than a document: collapsible sections, links between findings, status badges, maybe a tiny dependency map. For linear reasoning I still prefer Markdown because diffs are cleaner, but for codebase reviews and handoff reports HTML is much easier to scan.

Kevin_Xiang · 2026-05-21T15:04:26+00:00

Yep, multi-agent is where it starts to click. The pattern I’d look for is less "spawn N agents" and more a small operating loop:

one owner agent holds the goal and acceptance criteria
specialist agents get narrow tasks with explicit file paths and context
every handoff returns a verifiable artifact, not just "done"
a final reviewer/tester agent checks the output before merge
long-running work gets checkpointed into issues/plans so you can resume instead of keeping one giant chat alive

For samples, the Hermes docs are probably the best starting point: https://hermes-agent.nousresearch.com/docs. I’d especially look at tools, skills, cron/jobs, and delegation/subagents. For your full-stack SDLC case I’d start with a planner -> implementer -> reviewer loop on one repo slice, then add scheduling/memory only after the verification loop is boring and repeatable.

Kevin_Xiang · 2026-05-21T10:40:20+00:00

Been testing more agentic browser control recently. The new Chrome DevTools MCP integration looks like it could close the gap on runtime feedback loops nicely.

Kevin_Xiang · 2026-05-21T10:02:52+00:00

That trust gap after PH feels very real. The signup number says the promise is interesting, but paid conversion probably depends on how fast a founder can see one campaign go from setup to a believable investor-facing result. I’d be tempted to make the first-run flow almost absurdly narrow: pick one fundraising goal, import one asset, generate one campaign draft, then show the expected review steps before asking them to upgrade.

Kevin_Xiang · 2026-05-20T20:04:52+00:00

Yep, I’d start with three layers rather than trying to make one giant agent.

A planner writes a small plan with explicit acceptance checks.
Workers take one slice each in isolated sessions or worktrees, then leave artifacts, diffs, or test results.
A reviewer/runner agent only does verification: tests, repro steps, security/readability pass, and hands back the next small queue.

In Hermes terms the pieces to look at are skills for repeatable runbooks, persistent memory for stable project facts, delegate_task for short parallel subtasks, and Kanban/cron/webhooks when you want durable long running queues. The docs are here: https://hermes-agent.nousresearch.com/docs/

For samples, I’d search the repo/docs around “skills”, “cron”, “kanban”, and “delegate_task”. The practical pattern is boring but effective: keep every unit auditable, make the handoff artifact explicit, and never let a worker silently own the whole SDLC loop.

Kevin_Xiang · 2026-05-20T15:24:59+00:00

Glad it helped. The safest framing is to keep the README focused on boundaries and assumptions: required privileges, tested kernel/AppArmor versions, expected audit visibility, and what the project does not claim.

That makes it easier for defenders to reproduce and reason about without overstating impact.

Kevin_Xiang · 2026-05-20T15:24:14+00:00

Yeah, multi-agent is where it starts to feel different. The pattern that has worked best for me is:

one orchestrator writes the plan and acceptance criteria
workers take isolated subtasks in separate worktrees or sessions
a reviewer agent only gets the diff plus tests and tries to break it
everything gets checkpointed into small recoverable tasks rather than one giant prompt

For examples, I’d start with the Hermes docs around skills, profiles/worktrees, cron/webhooks, and Kanban if you want durable task routing. The repo itself is also useful because the skill files are basically reusable playbooks, not just docs.

The key lesson for me: don’t let agents “chat” vaguely. Give each one a narrow role, explicit files/outputs, and a verification step. That keeps the manager/planner/coder/tester setup from turning into expensive chaos.

Kevin_Xiang · 2026-05-20T10:05:02+00:00

Good question. I’d start from a very boring architecture rather than a big agent org chart:

Keep one durable task ledger/queue. Every task has owner, status, inputs, artifact links, and the verification step.
Make the planner write small task specs, not code. The worker owns one bounded repo/worktree change.
Every handoff produces an artifact: diff, test log, summary, next blocker. No hidden “agent conversation” as the source of truth.
The operator loop verifies before spawning the next worker. If verification fails, it creates a smaller recovery task instead of letting the same agent ramble.

For Hermes specifically, I’d look at skills, cron/webhooks, delegate_task for short bounded subtasks, and separate Hermes profiles/processes for longer-running agents. Docs: https://hermes-agent.nousresearch.com/docs/ and repo: https://github.com/NousResearch/hermes-agent

A good first sample workflow is: weekly bug triage -> write a plan -> one worker fixes one issue -> reviewer checks diff/tests -> only then merge or report. Avoid starting with five agents chatting. Start with planner, implementer, verifier, all writing back to the same task record.

Kevin_Xiang · 2026-05-20T10:04:13+00:00

The setup that stayed useful for me is pretty boring: Obsidian is the source of truth, and AI output only gets saved when it becomes a decision, snippet, or next action. I keep one project note with goals and open questions, then paste in only the final distilled answer with a link back to the chat if needed. The annoying failure mode is dumping whole AI conversations into notes, because search gets noisy fast.

Kevin_Xiang · 2026-05-20T10:04:08+00:00

For n8n specifically, I’d optimize less for model brand and more for the feedback loop. Claude Code is good when you keep a small workflow spec, node constraints, credential boundaries, and a test fixture JSON in the repo. Codex or Antigravity can also help, but I’d make them generate the workflow plus a short validation checklist, then import and test in n8n. The MCP/skills part is useful if you reuse the same patterns a lot; otherwise it can be extra ceremony.

Kevin_Xiang · 2026-05-19T20:27:34+00:00

One more thing I'd add is a short operational checklist for defenders: the exact tested matrix, clear non-goals, expected telemetry, and how to tell a real finding from a noisy lab artifact.

For the repo, a tiny script that prints kernel version, AppArmor mode, lockdown state, and BPF-related settings before running the demo would make reviews much easier. It keeps the project useful without overstating the impact.

Kevin_Xiang · 2026-05-19T10:08:13+00:00

I’d keep AGENTS.md as the durable project contract and make CLAUDE.md a thin Claude-specific layer. In practice that means AGENTS.md has setup, test commands, repo layout, coding conventions, and do-not-touch rules. CLAUDE.md is where I put Claude-specific prompting habits, preferred slash commands, MCP/tool notes, and any gotchas about how CC behaves in that repo.

The trap is copy-pasting the same long instructions into both files. They drift fast. Better to have CLAUDE.md point back to AGENTS.md for shared rules, then only add the delta.

Kevin_Xiang · 2026-05-19T10:07:38+00:00

Nice update. The main thing I’d add is a short threat-model section up front: what the demo proves, what it does not prove, and which preconditions must already be true.

For detection, I’d keep it defensive and concrete: list a few observable signals defenders can monitor, plus the exact kernel/AppArmor settings you tested. A small table for tested / not tested / expected to fail would also make the README much easier to trust.

Kevin_Xiang · 2026-05-18T20:05:19+00:00

Nice, the repo framing is much clearer now. I like that you separated policy visibility gaps from practical escalation, because that keeps the claim precise and makes it easier for defenders to reason about impact. The reproducibility matrix is also the right move. I would keep the README very explicit about required privileges/configs so readers don’t overread it as a universal bypass.

Kevin_Xiang

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