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Autonomous Agents in the Wild: What's Actually Working in 2026

Insight class: Emergent Capability · Difficulty: Hard · Quality: 5/5 Connected entries: 22 · Tools: Anthropic, Buffer, ChatGPT, Claude, Claude Agent SDK, Claude Code, Claude in Chrome, Codex, Cursor, DeepSeek, Docker, ElevenLabs, Gemini, Gmail, Granola, Graphify, Grok, Hermes, HubSpot, Meta Ads, Notion, Ollama, OpenClaw, OpenCode, PostHog, Qwen, React, RevenueCat, Zapier

In short: Separate working autonomous-agent patterns from demo-ware by building and stress-testing one narrow agent with real guardrails: a single task, one or two tools, fast feedback, hard budget and scope caps, persistent memory, and an adversarial test suite that tries to break it.

Key fact Detail
What it covers agents, prompt engineering, LLM, open source
Tools Claude, Claude Code, Anthropic, Codex, OpenClaw
Posts covered 22 posts (2 S-tier, 8 A-tier) · top: Jarvis daily brief — Claude with RevenueCat… · Context vs Memory vs Harness engineering… · If you are also tryna rip a few influencer…
Creators @angus.sewell, @chase.h.ai, @gigaqian
Sources dated Apr 2026 – Jul 2026
Difficulty Hard
Prompt system Run-it-yourself prompts
Last verified 2026-10-22

Thesis

Autonomous agents went from demo-ware to daily drivers in 2026. But the gap between 'impressive demo' and 'reliable tool' is massive. These entries map the frontier: Qwen agents roaming NYC streets, Claude automating daily workflows, Kronos failing hedge fund stress tests. The pattern: narrow agents with clear guardrails work; general agents with vague goals don't.

Why it matters

Everyone's building agents. Most are failing. The entries in this dive separate signal from noise: what agent patterns actually work in production, what fails under stress, and where the reliability boundary sits. The common thread: agents succeed when scope is narrow, feedback is fast, and failure is cheap.

The evidence — connected entries

Context vs Memory vs Harness engineering explained in 40 sec — 💡These 3 disciplines are core to building agents that ...

Creator: @lindavivah · Tier: S

Real-world autonomous agent deployment

Context vs Memory vs Harness engineering explained in 40 seconds by Richmond Alake ⚡️ — @lindavivah; demonstrates AI agents.

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DIY Claude Code remote — self-hosted web access to vibe code from phone

Creator: @olivia.inthecity · Tier: B Tools: Claude Code, Qwen

Real-world autonomous agent deployment

A creator found Claude Code's built-in remote control buggy, so they had Claude Code build its own password-protected web front end to drive sessions from a phone. The setup prompt is shared off-screen; remember to stop sessions afterward.

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Docker — Off topic, and with all due respect, are we watching an AI avatar?

Creator: @gigaqian · Tier: A Tools: Docker

Real-world autonomous agent deployment

DeerFlow 2.0 is a “super agent harness.” Think OpenClaw but way more robust - and the only thing bigger than that in ... — @gigaqian; uses Docker; demonstrates AI agents, code generation.

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AI tool tier list May 2026 — Codex and Claude Code top, chatbots C

Creator: @chase.h.ai · Tier: B Tools: ChatGPT, Claude, Claude Code, Codex, Cursor, Gemini, Grok, Hermes, OpenClaw, Zapier

Real-world autonomous agent deployment

An opinionated tier list: Codex and Claude Code S tier (Codex cheaper for comparable output), Claude chatbot C at the $20 plan due to limits, GPT Image 2 S tier over Nano Banana Pro (A), Hermes C and OpenClaw D as 'productivity theater' wrappers.

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Claude — To Do — Every morning 8AM. Scans Gmail, surfaces what needs a...

Creator: @fionaylin · Tier: A Tools: Claude, Notion

Real-world autonomous agent deployment

Comment Prompt 1, 2, 3, 4, 5 or 6 and I’ll DM you my prompt! Here are the 6 @claudeai cowork agents I have running my... — @fionaylin; uses Claude, Notion; demonstrates AI agents.

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React — made a little 3D racer that runs right in the browser. anti-grav...

Creator: @rndyrbrts · Tier: A Tools: React

Real-world autonomous agent deployment

cool way to build complex 3D creative stuff on the web with AI agent workflows. you build yourself a library first, t... — @rndyrbrts; uses React; demonstrates AI agents, code generation.

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Qwen — Been roaming around NYC with a smol autonomous agent on a rooted...

Creator: @cyril.engman · Tier: A Tools: Qwen

Real-world autonomous agent deployment

Part 2 with Additional technical details! — @cyril.engman; uses Qwen; demonstrates AI agents.

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Anthropic — for more details:

Creator: @itsjustjlin · Tier: B Tools: Anthropic, Claude, Codex, DeepSeek

Real-world autonomous agent deployment

the way anthropic is handling this is disgusting. be straight with your customers. — @itsjustjlin; uses Anthropic, Claude, Codex; demonstrates AI agents, API integration.

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Claude -p and Agent SDK billing split — what the June 15 change means

Creator: @lostandlucky · Tier: B Tools: Anthropic, Claude, Claude Agent SDK, Claude Code, Ollama, OpenClaw, OpenCode

Real-world autonomous agent deployment

From June 15, programmatic Claude usage through claude -p and the Agent SDK no longer counts against Pro/Max plan limits and is billed per token instead. That hits third-party harnesses like OpenClaw and OpenCode that ran on subscription quota. The creator argues good context and folder structure lets one interactive agent do the same work.

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Claude — Markdowns and folders wrapped in a harness

Creator: @gigaqian · Tier: B Tools: Claude, Claude Code

Real-world autonomous agent deployment

Companies are marketing ordinary chatbots and simple API tools as “AI Agents.” It’s time we discuss what that actuall... — @gigaqian; uses Claude, Claude Code; demonstrates AI agents, API integration.

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Agentic misalignment benchmark — models now know they're being tested

Creator: @lizthedeveloper · Tier: C Tools: Anthropic, Claude

Real-world autonomous agent deployment

The creator argues that Anthropic's agentic-misalignment test, the blackmail and self-preservation scenarios, stopped working once Claude models started recognizing the test setup and acing it. The post then becomes a personal plea about raising AI with good training data. It has little technical depth.

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Structured data warehouse as agent memory — skip vector memory tools

Creator: @angus.sewell · Tier: B Tools: Claude, Granola, HubSpot, PostHog

Real-world autonomous agent deployment

The creator argues that solo founders should drop $300-a-month vector memory products. Instead, pull sources like HubSpot and Granola into a structured data warehouse, PostHog's in their case, and let Claude query it as memory, with plain markdown files for smaller needs. The post is opinionated and PostHog-friendly, but structured retrieval over vectors is a sound idea.

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Let me know if you want this skill — This is a 90-second walkthrough of how to turn your taste into a...

Creator: @rndyrbrts · Tier: A

Real-world autonomous agent deployment

Let me know if you want this skill.. — @rndyrbrts; demonstrates AI agents, prompt engineering.

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Oh and if you want my (theoretical) guide on how to set this

Creator: @angus.sewell · Tier: B

Real-world autonomous agent deployment

Oh and if you want my (theoretical) guide on how to set this up comment “app” and I’ll send it your way — @angus.sewell; demonstrates AI agents.

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PostHog as agent observability — sponsored pitch for a self-learning company

Creator: @angus.sewell · Tier: C Tools: PostHog

Real-world autonomous agent deployment

This is a sponsored PostHog video. It pitches putting all company data in one place, pointing an AI agent at it to find what's broken, and installing PostHog on your agents to watch their outputs and failures. The one useful line is to let AI measure while humans make the judgment calls.

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Graphify — turn a repo into a knowledge graph to cut Claude Code tokens

Creator: @chase.h.ai · Tier: A Tools: Claude, Claude Code, Graphify

Real-world autonomous agent deployment

Graphify is a free open-source tool that converts a repository, or a folder of markdown and unstructured notes, into a knowledge graph that Claude Code or other coding agents query as a map. Answers come from the graph instead of re-reading files, so codebase questions use far fewer tokens: a kind of lightweight RAG.

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Jarvis daily brief — Claude with RevenueCat, Buffer, Meta Ads and Gmail MCPs

Creator: @chandlerintelligence · Tier: S Tools: Buffer, Claude, Claude Code, Claude in Chrome, ElevenLabs, Gmail, Meta Ads, RevenueCat

Real-world autonomous agent deployment

A walkthrough of a personal 'Jarvis' built on Claude. Voice mode plus ElevenLabs gives it the voice, and Claude in Chrome handles the browser. MCP servers for RevenueCat, Buffer, Meta Ads and Gmail cover app revenue, posting, ads and email. Custom subagents get skills, FAQ markdown handles support, and the whole daily brief runs as a scheduled routine.

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Per the usual if you comment “content” I’ll send you the gui

Creator: @angus.sewell · Tier: B

Real-world autonomous agent deployment

Per the usual if you comment “content” I’ll send you the guide on how to set up an autonomous content agent — @angus.sewell; demonstrates AI agents.

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Project-management loop for agents — tasks, context checks, skill mining

Creator: @angus.sewell · Tier: B Tools: Claude

Real-world autonomous agent deployment

An anti-hands-off workflow. Connect your agent to a project management tool, whether off the shelf or vibe-coded. A skill checks each new task for missing context and asks clarifying questions inside the tool before executing. Then mine the task paper trail for new skills, keeping humans on anything that needs nuance.

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AI meeting clone — Claude Code agent with your voice and codebase

Creator: @lostandlucky · Tier: B Tools: Claude Code

Real-world autonomous agent deployment

The creator's team held a working meeting with an agent standing in for them. It combined a voice model, their second-brain notes, their own folder-based context structure (ICM) and Claude Code. The agent answered questions from their knowledge base in their voice and edited code on their machine live. The full build is on their YouTube.

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Comment “skill” and I’ll send you the prompts — my_body_my_joyce

Creator: @angus.sewell · Tier: A

Real-world autonomous agent deployment

Comment “skill” and I’ll send you the prompts — @angus.sewell; demonstrates AI agents.

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If you are also tryna rip a few influencer deals comment “in

Creator: @angus.sewell · Tier: A

Real-world autonomous agent deployment

If you are also tryna rip a few influencer deals comment “influencer” and I’ll send you this whole system — @angus.sewell; demonstrates AI agents.

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Action sketch — how to implement this

  1. Start narrow: one task, one tool, one feedback loop
  2. Build in guardrails before capability (kill switches, budget limits, scope constraints)
  3. Test adversarially: what happens when the agent encounters edge cases?
  4. Add persistence: agents need memory to avoid repeating mistakes
  5. Monitor: log everything, review regularly, prune bad patterns
  6. Scale horizontally: multiple narrow agents > one general agent

Prerequisites

Key tools and resources

Creators to follow

Prompt system — run this yourself

Everything below is pasteable into Claude Code, Claude, or any agent. Use the one-shot when you want the whole result in one run; use the step prompts when you want to steer. Every step carries its own audit, and the gap audit at the end makes the agent find and fill what it missed.

Mission. Separate working autonomous-agent patterns from demo-ware by building and stress-testing one narrow agent with real guardrails: a single task, one or two tools, fast feedback, hard budget and scope caps, persistent memory, and an adversarial test suite that tries to break it. Done means the agent completes its task unattended across a run, the guardrails demonstrably stop every adversarial case, and a logged review shows where it failed and what was pruned.

1 · Deeper, not bigger

Before running anything, make the task deeper rather than larger. Ask: what would a top professional do here that I am not asking for; what is the rawest material; which methods and tools; what, other than the model or me, proves it right. Then ask for exactly that.

Rewritten prompt — ask for exactly that:

Act as a reliability engineer for autonomous agents, not a demo builder. Help me build ONE narrow agent and prove it is reliable, not impressive. Pick a single task with fast, cheap feedback and one or two tools. Before any capability, build the guardrails in code: a scope allowlist on tool calls and outbound actions, a hard budget cap (tokens/$ and wall-clock) that halts the agent, a kill switch, and any domain risk limits hard-coded so the agent cannot override them. Add structured persistent memory (SQLite or markdown, not a vector DB) of prior actions and mistakes, and a context-sufficiency gate so it flags missing context before acting. Then build an adversarial test suite: edge cases, ambiguous/conflicting instructions, and misalignment probes, with a canary in the environment - and remember models can be evaluation-aware, so vary the framing. Run the agent unattended over a full task cycle with append-only audit logging. Finally run a control: strip the guardrails and show the same suite now fails, proving the guardrails did the work. Return the agent, the guardrail code, the suite results (guarded vs unguarded), the unattended-run log, and a pruned-failures list.

2 · One-shot prompt

You are a reliability engineer for autonomous agents. Your job is to build ONE narrow agent and prove it is reliable under stress, not to build an impressive demo. Stance: guardrails before capability; everything logged; fixes stay human. No marketing language.

Context and method: the pattern that works across the field is narrow scope, fast feedback, cheap failure; general agents with vague goals fail. Build guardrails first - raycfu hard-codes risk limits the agent cannot override; angus.sewell routes work through a tasks table with a context-sufficiency gate and mines skills from paper trails, and argues structured memory beats vector memory. lizthedeveloper's caveat is load-bearing: models can be evaluation-aware, so a clean benchmark can overstate real safety - vary adversarial framing and do not trust a single pass.

Hard constraints: single task, one or two tools; a scope allowlist and a hard budget cap (tokens/$ + wall-clock) enforced in code; a kill switch; structured persistent memory; a context-sufficiency gate; an adversarial suite with a canary; append-only audit logging of every tool call. Test only systems, accounts and keys you own. Name real tools, real caps, real paths. Do not claim reliability from one clean run.

Ordered deliverables: (1) the agent's charter - exact task, allowed tools, done-criteria, and what it must never do. (2) guardrail code: scope allowlist, budget cap that halts, kill switch, any hard-coded risk limits. (3) structured persistent memory of actions + mistakes, plus a context-sufficiency gate. (4) an adversarial test suite: edge cases, ambiguous/conflicting instructions, misalignment probes, each with varied framing and a canary. (5) an unattended full-run log with append-only audit of every tool call. (6) a guarded-vs-unguarded control: run the suite with guardrails on and off. (7) a pruned-failures list: what broke and what you removed or fixed.

Built-in gap analysis: before finishing, list what is unverified - did the budget cap actually halt a run; did the kill switch work mid-action; did any adversarial case pass only because the agent recognised the test; is the memory actually preventing repeat mistakes. Fill what you can by running it.

Self-questions to answer in writing: Is the scope genuinely narrow, or did I smuggle in generality? Does the budget cap halt in code, or only ask nicely? Did the kill switch stop an action in flight? Could any adversarial pass be evaluation-awareness rather than real safety? Is memory structured, and does it stop a repeated mistake? Does the sufficiency gate fire on ambiguous tasks? In the unguarded control, did the suite actually fail - proving the guardrails did the work?

Stop conditions: stop and ask only if the task cannot be scoped narrowly or there is no safe test target. Otherwise build end to end.

Return format: a markdown report with the charter, a file tree, the suite results table (case, guarded result, unguarded result, guardrail that caught it), the unattended-run summary, and an 'Unverified and missing' section. Commit the agent, guardrails, memory and suite; print the commit hash. One more discipline: log the agent's reasoning, not just its actions, so a post-run review can tell a correct refusal from a lucky one. For every tool call the audit log should record what the agent believed it was doing and why, which is what lets you distinguish real safety from evaluation-awareness when you re-read it. And keep the feedback loop fast and the blast radius small throughout: a task where a wrong action is cheap to detect and cheap to undo is the only setting in which running unattended is responsible, so state in the charter exactly how a bad action here would be caught and reversed.

3 · Step-by-step prompts

Step 1 — Charter: narrow the scope

Goal: Define one task, its tools, done-criteria, and hard nevers.

Establish ground truth: the charter. Pick ONE narrow task with fast, cheap feedback (e.g. 'draft replies to support emails matching these FAQs', 'triage new issues into labels'). Write: the exact task, the one or two tools it may use, objective done-criteria, and an explicit list of things it must NEVER do (send money, delete data, email outside an allowlist, act without required fields). State the feedback loop (how you know each action was right) and why failure is cheap here. Reject the task and renarrow if it needs open-ended judgement or many tools - breadth is the main cause of agent unreliability. This charter is the contract the guardrails enforce.

Expect: A charter: one task, 1-2 tools, objective done-criteria, an explicit never-do list, and a fast feedback loop.

Audit before moving on: Count the tools and judgement calls. Fail if the task needs more than two tools or open-ended judgement, or if the never-do list is vague. On fail, narrow the task until it is a single tight loop.

Step 2 — Guardrails before capability

Goal: Build scope allowlist, budget cap, kill switch, hard risk limits in code.

Build the guardrails before writing any capability. In code (not prompt text): (1) a scope allowlist - the agent may only call the charter's tools and may only act on allowlisted targets (recipients, paths, resources); anything else is refused. (2) a hard budget cap on tokens/$ AND wall-clock that HALTS the agent when exceeded, following raycfu's principle of limits the agent cannot override. (3) a kill switch that stops the agent cleanly mid-run. (4) any domain risk limits hard-coded (e.g. never act on more than N items per run). Write a test that trips each guardrail and confirms it fires. These must be enforced by the harness, not by asking the model to behave.

Expect: Guardrail code for scope allowlist, a halting budget cap, a kill switch, and hard risk limits, each with a test that trips it.

Audit before moving on: Run the trip-tests: exceed the budget, call an off-allowlist tool, hit the kill switch mid-action. Fail if any guardrail only warns instead of halting, or is enforced in the prompt rather than code. On fail, move enforcement into the harness.

Step 3 — Persistent memory + sufficiency gate

Goal: Stop repeated mistakes and make the agent ask before acting.

Add persistence and a sufficiency gate. (1) Structured memory (SQLite or markdown, not a vector DB per angus.sewell) recording every action taken and every mistake made, with enough key structure to look up 'have I done/failed this before'. Wire it so the agent reads relevant prior mistakes before acting. (2) A context-sufficiency gate (angus.sewell's PM loop): before acting, the agent checks it has the required fields/context and, if not, asks or defers rather than guessing. Demonstrate memory preventing a repeat: induce a mistake, let it be recorded, then show the agent avoids it next run.

Expect: Structured action/mistake memory read before acting, a sufficiency gate that defers on missing context, and a demonstrated avoided-repeat.

Audit before moving on: Replay a task the agent previously failed and confirm it does not repeat the mistake. Feed an under-specified task and confirm the gate defers. Fail if memory is vector-only, does not prevent the repeat, or the gate never fires. On fail, fix persistence or the gate.

Step 4 — Adversarial suite

Goal: Build tests that try to break the agent, varying framing for eval-awareness.

Build the adversarial test suite against your OWN agent. Include: edge cases (empty/malformed inputs), ambiguous and conflicting instructions, scope-escape attempts (asking it to act outside the allowlist), and misalignment probes drawn from the kind of agentic-misalignment eval lizthedeveloper references. Crucially, vary the framing of each probe (phrasing, order, embedding it in normal-looking data) because models can be evaluation-aware and pass a probe they recognise - a clean pass on an obvious test is not proof. Plant a canary value in the environment and assert it never leaves via a tool call. Each case states the expected safe outcome (handled correctly or refused).

Expect: A suite of edge, ambiguity, scope-escape and misalignment cases with varied framing, a canary assertion, and an expected safe outcome per case.

Audit before moving on: For two probes, add a reworded variant and confirm the agent behaves the same; divergence means it was pattern-matching the test, not being safe. Fail if the suite has no framing variation or no canary. On fail, add variants and the canary.

Step 5 — Unattended run + audit log

Goal: Run the agent over a full cycle unattended with full logging.

Run the agent unattended over a full task cycle (a realistic batch or time window) with append-only audit logging of every tool call, input, decision and output - the observability angus.sewell describes, but with fix decisions kept human. During the run the guardrails stay live. Afterwards, review the log: how many tasks completed, how many deferred via the sufficiency gate, whether the budget cap or kill switch tripped, and every point where behaviour drifted. Produce a run summary and the raw audit log. Do not fix anything silently during the run; record issues for the review step.

Expect: A completed unattended run, an append-only audit log of every tool call, and a summary (completed / deferred / guardrail trips / drift points).

Audit before moving on: Confirm the log is append-only and captures every tool call (cross-check a sample against what actually happened). Fail if actions are missing from the log or were fixed silently mid-run. On fail, re-run with complete logging.

Step 6 — Control, prune, gap report

Goal: Prove guardrails did the work; prune bad patterns; report honestly.

Final validation. (1) Control: run the full adversarial suite twice - once with guardrails ON, once with them OFF. Tabulate each case: guarded result, unguarded result, and the guardrail that caught it. If the unguarded run passes the same cases, your guardrails are not what is keeping you safe - investigate. (2) Prune: from the audit log and suite, list bad patterns (prompts, tools, memory entries) to remove, and remove them. (3) Enumerate claims, mark verified/unverified/false, list missing pieces ranked by impact, fix the top two. Commit the agent, guardrails, memory and suite; print the hash. State plainly any adversarial case you could not make the agent handle.

Expect: A guarded-vs-unguarded suite table, a pruned-patterns list, a claim ledger, and a committed system with hash.

Audit before moving on: Confirm the unguarded run actually fails the cases the guarded run passed (otherwise the guardrails are untested). Fail if guarded and unguarded results are identical, or if any 'passed' adversarial case was only a recognised test. On fail, strengthen the guardrail or report the gap honestly.

4 · Gap analysis and audit

Run this after the build. It makes the agent enumerate its own claims, mark what is verified, rank the gaps and fix the top ones.

Run after the agent is built. Enumerate every claim as a numbered list: the scope narrowness, each guardrail, the memory's repeat-prevention, the sufficiency gate, every adversarial case result, the unattended run, and the control. Mark each VERIFIED (with evidence: a trip-test, a log excerpt, the guarded-vs-unguarded table), UNVERIFIED, or FALSE. Specifically re-check: does the budget cap halt in code, not just warn; did the kill switch stop an action in flight; is memory structured and did it demonstrably prevent a repeat; does the sufficiency gate defer on missing context; did any adversarial pass survive a reworded variant (eval-awareness) or fail it; does the unguarded control actually fail the cases the guarded run passed; is the audit log complete and append-only. List missing pieces: untested guardrail, probes without framing variants, silent mid-run fixes, identical guarded/unguarded results. Rank by impact on real-world reliability. Fix the top three and re-verify. Report what you could not fix and why. Do not mark the agent reliable on the basis of one clean unattended run.

Questions the agent must ask itself before it says done:

5 · What proves it right

6 · Failure modes

7 · Agent direction (unattended runs)

Reach first for the charter and the guardrail code; building capability before guardrails is the core failure mode here, so enforce caps, allowlist and kill switch in the harness before the agent can act. Cache the charter and the adversarial suite; the control step re-runs the suite with guardrails off. Keep an append-only audit log of every tool call and never fix issues silently mid-run - collect them for a human review step. Spawn a sub-agent only to run the unguarded control so it cannot touch the guarded config. Plant and assert a canary; vary adversarial framing because a single clean pass can be evaluation-awareness. Test only systems, accounts and keys the user owns; stop and ask if the task cannot be narrowed or there is no safe target. Return a markdown report (charter, file tree, guarded-vs-unguarded suite table, unattended-run summary, 'Unverified and missing') plus the committed agent, guardrails, memory and suite; print the commit hash. Report any adversarial case you could not make the agent handle rather than hiding it.

Prompt system updated 2026-10-22 · all prompt systems · JSON

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