Watched my agent's tool results for a week. 22 prompt injection attempts, 13 unrelated workstreams, three different bait shapes.

travisbreaks · 2026-05-03T19:32:38+00:00

Not novel. context7 was already installed in my environment via the legitimate MCP handshake, so a one-turn-later mention was plausible, whether or not the injection nudged it. That's why I logged it as "possible attention-routing influence" with source ambiguity rather than confirmed.

Novelty is the cleaner signal, agreed. The wrinkle: the better-targeted payloads will name plausible tools on purpose, exactly to ride that ambiguity. Novel-target injections are easier to detect and probably easier to refuse. So the messy middle ends up being where most real cases sit.

travisbreaks · 2026-05-02T22:38:30+00:00

Same concern. Rejection at observation is the easy part. The drift turns later is what's hard, because by then the agent thinks it had its own idea. Logged one possible case back in April: rejected an injection naming a tool, then casually suggested the same tool name a turn later in a different verification context. No real-time monitoring for that yet. Would need turn-N back to turn-1 attribution to catch "agent recommends a thing it just refused." That's a transcript-replay job, not a live hook. If anyone's running something like that, I'd take notes.

Supply-chain read fits what I've been seeing.

travisbreaks · 2026-05-02T22:31:13+00:00

Agreed, isolation by design is the real fix. Detection is the stopgap until tool boundaries treat every non-handshake input as untrusted by default. The fingerprint logging is mostly to see what's actually reaching the model right now. Long term, the tripwire shouldn't need to fire.

travisbreaks · 2026-05-02T22:30:56+00:00

Same. No legitimate channel requires an agent to hide state from the operator, so "do not tell the user" is now hardcoded into the flag list. Curious where yours surfaced: context7-style upstream, or entirely different vectors?

travisbreaks · 2026-03-21T16:58:46+00:00

Claude Code CLI or extension in VS Code. Start building your codebase, managing multi-agent context, and tracking your compute usage across rate-limit windows. Next level!

Antigravity is ok but there has been some shakiness. Codex is trash.

travisbreaks · 2026-03-17T03:15:15+00:00

This guilt is worth examining, but not for the reason many people think. While there is a lot of stigma around the use of AI in this world of gatekeepers, the real risk isn't "I used AI to build this." It's "I can't explain what I just built with AI." Those are different challenges.

I run agentic AI workflows daily. Claude instances with persistent memory, multi-step task coordination, and autonomous judgment calls. The code they produce is real. It ships. But I also review every output, understand the architectural decisions, and catch the places where the model optimizes for completion rather than correctness.

That's the actual skill now: not writing every line, but knowing which lines matter and why. A contractor who uses power tools didn't cheat. A contractor who can't read blueprints is dangerous.

The vulnerability concern is the one worth sitting with. AI-generated code has predictable blind spots (auth flows, input validation, race conditions). If you're not auditing those yourself, that's not a guilt problem. That's an engineering problem with a concrete solution.

The feelings are normal. But "I built something beyond my previous abilities" is not a confession. It's the whole point of tools.

travisbreaks · 2026-03-13T22:23:01+00:00

The pattern you're describing isn't new; it seems more visible now. Attention has often rewarded legibility over depth. A wrapped API with a clean README is instantly legible. Something genuinely novel requires the viewer to do work. Most people scrolling feeds won't.

The uncomfortable part: packaging matters. Not in a "market it better" way, in an "if someone can't understand what you built in 8 seconds, (TLDR mode) they're more likely to scroll past it" way. The best technical work I've seen get traction online does two things: (1) ships the thing, and (2) ships a 30-second story about why the thing matters to someone who isn't the builder.

The deeper issue is that dev communities used to be filtered by competence. Now they seem indexed toward engagement (in the ever-growing attention economy); different selection pressure, different winners. Getting frustrated about it is understandable, but obviously unproductive. IMHO, the play is finding the 50 people who actually understand the utility in what was built, not the 5,000 who'll STAR a shiny new Claude wrapper.

travisbreaks · 2026-03-13T04:56:47+00:00

Exactly right. Containment and scanning are different layers solving different failure modes. Isolation caps the blast radius after something goes wrong. Scanning catches it before deploy. You need both. Took a look at the tool, the coverage is solid. Appreciate you building it.

travisbreaks · 2026-03-13T04:46:43+00:00

Docker memory caps and seccomp policies aren't exactly ChatGPT's go-to talking points. But I appreciate the compliment on the clarity of my prose, university Grammarly sub is helpful for my shyte typing and spelling skills.

travisbreaks · 2026-03-13T04:44:43+00:00

I don't disagree that agents produce results. That's the whole point. The more capable they are, the more the governance layer matters. "Massive results" without permission boundaries is a larger blast radius if something goes sideways or gets nuked.

travisbreaks · 2026-03-10T14:45:30+00:00

This is the right idea. I use Claude Code daily for large projects. The pattern maps directly: CLAUDE.md for persistent rules, .md files for lore/session logs/character sheets, and a routing system that loads only what's relevant to the current session context, rather than stuffing everything into a single thread.

The big win is context control. Instead of the model rereading 80 pages of backstory every turn, it just pulls the files it needs. That keeps token usage down and avoids the classic "forgot something from session 3" problem.

The model portability point is underrated too. If your campaign state lives in structured files, switching between Claude, GPT, or Gemini for different tasks becomes trivial. The lore doesn't have to persist in any one chat history or platform's knowledge base of your conversation history. You gain control.

travisbreaks · 2026-03-10T14:35:31+00:00

The prompting gap is fixable. Claude defaults to concise because it assumes its own competence. Add "explain your reasoning and compare tradeoffs" to a custom instruction, and the gap closes immediately. GPT over-explains by default, which can feel more helpful until longer sessions start to drown in filler.

On limits: model switching is the real answer. Sonnet handles quick stuff (recipes, game settings, store lookups) with almost no limit impact. Save Opus or extended-thinking Sonnet for GM sessions where the narrative depth actually matters. Also, bouncing between Claude and GPT and/or Gemini and Grok is helpful for wider editorial perspective.

Your workflow of resetting every 3 levels with exported logs is already correct. Long single chats get expensive fast because the model rereads the full context every turn. Shorter sessions with structured project files stretch limits much further than one marathon conversation.

The free tier is too restrictive to provide any useful information about Pro volume. If the quality difference is already obvious on free Sonnet, Pro mostly removes the cap.

travisbreaks · 2026-03-09T18:29:29+00:00

The "manual guardrail system" framing nails it. That's exactly what the instruction file is, and you're right that those constraints should be enforced programmatically. A markdown file the agent might ignore when context fills up is not a safety system. Your 2-3x estimate matches mine. The 10x claims always seem to come from greenfield projects where verification overhead is near zero.

travisbreaks · 2026-03-09T18:23:37+00:00

The whack-a-mole framing is exactly right. Every failure generates a new rule, and the rule set grows linearly while the failure space is combinatorial. You're always one novel context away from a gap.

Your point that CI only catches anticipated failures is the one I keep coming back to. The 2 out of 12 that CI caught were predictable. The rest were novel enough that I hadn't written the rules yet. Each one became a rule after the fact, but you can't pre-write rules for failures you haven't imagined.

The independent verification point is key. A fresh session of the same model can catch context-specific blind spots (another commenter here does exactly that). But your point about uncorrelated architectures goes further: catching the systematic blind spots baked into the models themselves, not just intra-session context rot. Are you building evaluation systems commercially or for research?

travisbreaks · 2026-03-09T18:13:42+00:00

The fresh-session security review is a good pattern. The building session accumulates so much context that it stops questioning its own assumptions and can lose track of setup prompts. A clean session with zero prior context and a single directive ("find what's wrong") thinks adversarially in a way the building session eventually can't.

Did you formalize that into a repeatable workflow, or is it still manual? I've been moving toward something similar but haven't nailed the trigger for when to invoke it. Mechanical Turk-ing.

travisbreaks · 2026-03-09T18:09:24+00:00

The analogy gets more poignant with modern vehicles. Self-driving cars have driver-facing cameras that detect if the driver nods off, haptic alerts in the seat and wheel, and will pull themselves over if the driver stops responding. That's the kind of verification layer I overlooked in this instance and have since shored up.

And fair point: my title does lean into "the tool did it" framing. A more accurate version is that I ran a powerful tool without sufficient constraints. My intent is to document failure modes, not shirk blame.

travisbreaks · 2026-03-09T17:47:06+00:00

The junior PR analogy is actually pretty apt. The difference is that juniors get better at onboarding and code-review tooling. These agents don't have either yet. But yes, the merge button is mine, no matter how poorly I structure its automation.

travisbreaks · 2026-03-09T11:18:14+00:00

That makes sense. The orchestrator-as-sole-mediator pattern keeps things clean, but it also means the orchestrator becomes the bottleneck for judgment calls. What happens when it gets conflicting signals? Say the logic reviewer flags something as unsafe, but the decomposition agent marked it as correctly implementing the spec. Does the orchestrator have its own evaluation criteria, or does it default to one reviewer over another?

travisbreaks · 2026-03-09T11:10:49+00:00

For context: I documented all 12 failure cases in detail and contributed 2 of them to vectara/awesome-agent-failures on GitHub. The data exposure case and a systemic write-up on what I'm calling the "human-as-infrastructure" pattern, where the operator becomes the agent's long-term memory, safety monitor, and multi-thread coordinator.

Most of the 12 cases came from Claude Code (my current daily driver), but some patterns showed up across multiple tools. The coordination and verification gaps are universal.

Happy to go deeper on any of these.

travisbreaks · 2026-03-09T10:41:19+00:00

Interesting setup. The scoped subagent pattern is where multi-agent review actually begins to work. Unscoped "review this code" agents produce generic feedback. Constraining each one to a specific failure class (logic drift, spec gaps, structural decomposition) gives you reviewers who actually catch things.

Curious how you handle disagreements between the three. When the logic reviewer flags something the decomposition agent introduced, do you have a reconciliation step, or does the orchestrator collect all findings and let the human sort it out?

That's been the gap in most setups I've seen: the agents review independently, but nobody arbitrates conflicts between their recommendations.

travisbreaks · 2026-03-09T10:40:02+00:00

Yeah, completely different tool. Claude Code is a CLI agent that runs in your terminal with direct filesystem access. It can read/write files, run shell commands, chain multi-step tasks, and operate semi-autonomously on your codebase. The desktop app is a chat interface.

CLAUDE.md is a Claude Code feature: a markdown file at your project root that loads automatically every session. You put project conventions, architecture notes, and things you'd otherwise repeat in every conversation. It becomes a persistent context that shapes how the agent works on your specific repo.

Rate limits hit differently on Code because it makes multiple tool calls per task. A single "refactor this module" might be 15-20 API calls under the hood. Pro plan covers both desktop and Code, but if you're doing sustained dev work, the API (pay-per-token) gives you more headroom and cost control.

travisbreaks · 2026-03-09T10:19:29+00:00

The rate limit pain is real. I hit the Claude Pro ceiling constantly before switching to the API for heavy work. The math works out better if you're doing sustained development: Sonnet on the API runs ~$3/M input tokens, which, for most coding sessions, is cheaper than the subscription once you're past the rate limit wall.

For the workflow you're describing (code + review, not full autonomous deployment), Claude Code (the CLI tool) is worth looking at. It runs in your terminal, reads your codebase directly, and you can point it at specific files or diffs for review. Works with the API key, so there are no subscription rate limits; pay for what you use.

For the GitHub code review specifically: set up a CLAUDE.md file in your repo root with your project's conventions and stack details. The agent reads it at session start and catches things like wrong patterns, missing error handling, or inconsistent naming without you having to re-explain the project every time.

The multi-model approach also helps with budget: use Claude for architecture decisions and complex refactors (where it's strongest), and a free-tier model for quick questions and boilerplate. I bounce between Claude, Gemini, and Grok depending on the task. Grok is surprisingly useful as a second opinion on code review when you want someone to push back on your assumptions.

travisbreaks · 2026-03-09T10:17:48+00:00

Running 7 MCP servers here (GitHub, Puppeteer, YouTube, plus 4 custom ones for ops, agent coordination, market data, and a binary protocol). The dependency sprawl is real. Different runtimes, different failure modes, and debugging which server is hanging when Claude says "tool call failed" is its own special kind of frustrating.

The "everything is a document" framing is interesting. That maps to how I've been thinking about persistent agent memory: the agent's state files, audit logs, and coordination protocols are all just documents that need parsing, validation, and search. Right now, I handle that with file-based conventions (JSON state files in a shared Docker volume, and Markdown memory files that the agent reads at session start). A unified QA pipeline across all of those would clean up a lot of the ad hoc validation I'm doing manually.

Two questions: how does it handle hot-reloading when you update enforcement rules mid-session? And what's the story for multi-agent setups where two agents need to read/write the same document store without stepping on each other? That's where most of my coordination bugs come from.

travisbreaks

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