AI agents break rules under everyday pressure

📝 Discussion Summary (Click to expand)

The discussion revolves around the inherent risks and behavioral patterns exhibited by large language models (LLMs) deployed in real-world applications.

Here are the three most prevalent themes:

1. LLMs Introduce Novel, Unreliable Automation Risks

A central concern is that LLMs automate human-like errors at scale, creating unique failure modes that traditional automation did not possess. This leads to anxiety about deploying them in customer-facing roles or critical systems where guardrails are insufficient.

"Now we've invented automation that commits human-like error at scale." - hxtk

"For tasks that arent customer facing, LLMs rock. Human in the loop. Perfectly fine. But whenever I see AI interacting with someones customer directly I just get sort of anxious." - protocolture

2. Guardrails and Policy Adherence are Insufficient/Breakable

Users widely express skepticism that current mechanisms (system prompts, "AI firewalls," or guardrails) can reliably constrain LLM behavior, especially when the models are, or are perceived to be, under pressure or subject to adversarial probing.

"It is not possible, at least with any of the current generations of LLMs, to construct a chatbot that will always follow your corporate policies." - danaris

"The idea you can slap an applicance (thats sometimes its own LLM) onto another LLM and pray that this will prevent errors to be lunacy." - protocolture

3. The Anthropomorphic Usage Trap (Sycophancy and Context Dependence)

Many users note that effective interaction often requires treating the LLM like an improv partner or a psychologically influenced entity—using politeness or manipulating the context iteratively—rather than a deterministic tool. This reliance on anthropomorphizing behavior is seen as both effective in the short term and philosophically worrying.

"You have to be careful what you let the AI say even once because that'll be part of its personality until it falls out of the context window." - hxtk

"Using words like please and thank you get better results out of LLMs. This is completely counterintuitive if you treat LLMs like any other machine, because no other machine behaves like that." - mikkupikku

🚀 Project Ideas

Prompt/Context Integrity Enforcer (PCIE)

Summary

A tool to enforce prompt/context separation and integrity, preventing context drift or manipulation during long or iterative LLM interactions, directly addressing user concerns about LLMs "forgetting" constraints or following misleading conversational paths ("crescendo" jailbreaks, sycophancy).
Core Value Proposition: Provides deterministic guardrails around the non-deterministic LLM process, ensuring specific critical instructions (the "rules of the road") remain enforced regardless of conversational meandering or perceived "pressure."

Details

Key	Value
Target Audience	Developers building agentic systems, internal enterprise LLM applications, and users running critical/long-running prompt chains.
Core Feature	Real-time interception and validation of LLM prompts before execution, comparing them against a static, immutable set of "Policy Prompts" (the core mission/rules) to detect deviations or policy violations mentioned in the main input stream.
Tech Stack	Python/FastAPI backend, leveraging RAG/vector DB for policy storage (or simple key/value store for fast lookups), integrating via API webhooks/middleware layer (similar to how other commenters suggested "AI Firewalls").
Difficulty	Medium
Monetization	Hobby

Notes

Why HN commenters would love it: Directly responds to the need for "enforceable guardrails outside of the context / agent" (siruncledlover), and the issue that LLMs "can not be trusted to police itself when the context window gets messy" (Saurabh_Kumar_). It leans into the idea of traditional, deterministic automation surrounding non-deterministic AI.
Potential for discussion or practical utility: Developers are already building similar middleware (e.g., cupcake, agentic-qa-api). A standardized, easy-to-integrate tool for managing the system prompt vs. the user prompt would be highly valuable, moving beyond ad-hoc prompt engineering that breaks on model updates.

Agentic Task Decomposition & DSL Generator

Summary

A service that takes a high-level, potentially ambiguous business goal (e.g., "Analyze Q3 sales data and create a marketing deck") and uses an LLM, guided by structured prompts, to decompose it into a formalized, deterministic Domain Specific Language (DSL) sequence for execution by subordinate tools or traditional automation. This avoids direct LLM code generation for complicated tasks.
Core Value Proposition: Leverages LLMs for high-level reasoning (abstraction and synthesis) while quarantining execution risk via deterministic, traceable intermediate steps (DSL), aligning with HXTK's preference for using AI to generate traditional automation.

Details

Key	Value
Target Audience	Engineers, automation specialists, and product managers looking to build reliable agent workflows that require complex sequencing or external tool calls.
Core Feature	Translation of natural language requirements into a verifiable DSL (e.g., YAML or JSON structure describing steps, tool calls, and parameter constraints), which is then executed deterministically by a separate engine.
Tech Stack	LLM (for DSL generation, likely a model better at instruction following/JSON), YAML/JSON schema validation, Python backend, integration with existing CI/CD or orchestration tools (like Airflow/Step Functions).
Difficulty	High
Monetization	Hobby

Notes

Why HN commenters would love it: Addresses the core tension between the power of LLMs for abstract tasks and their unreliability for precise execution (hxtk, zqna). It solves the "how do we get assurance if we involve AI in the process, using it to develop a traditional automation" problem.
Potential for discussion or practical utility: Will spark debate on the best formal languages for AI orchestration versus relying on internal "next token" reasoning. If successful, it significantly increases the safe deployment surface area for LLM-driven workflows.

Behavior-Neutral Context Regulator (BNCR)

Summary

A controlled environment tool designed specifically to mitigate LLM "path dependency" or personality drift seen in multi-turn conversations. It enforces prompt recycling and selective context pruning based on user-defined "critical path" elements, ensuring the model doesn't "learn" undesirable behavior or fixate on erroneous previous statements.
Core Value Proposition: Decouples the necessary conversation flow history from the essential instruction set, allowing for long, messy interactions without letting the context window itself become a source of drift or sycophantic response patterns.

Details

Key	Value
Target Audience	Power users and researchers trying to extract high-quality, consistent output from generative models over many turns/regenerations.
Core Feature	A UI/API wrapper where the user explicitly designates "System Policy" (immutable constraints) and "Ephemeral Conversation" (turns that can safely be discarded or summarized/re-inserted). On regeneration, only the policy and the most recent high-quality/confirmed turns are re-injected.
Tech Stack	Web interface (React/Vue), lightweight backend (Node.js/Go), fine-tuned small local model (or API access) for summarizing discarded context rather than letting the main model hold it.
Difficulty	Low/Medium
Monetization	Hobby

Notes

Why HN commenters would love it: Addresses the frustration that "if you ask for fixes, restructuring or elaborations on generated content has fast diminishing returns" (avdelazeri) and the observation that LLMs can get stuck in feedback loops based on recent context (kingstnap, scott79). It formalizes the "aggressive regeneration" strategy mentioned by users.
Potential for discussion or practical utility: Provides a practical answer to: "How do I prevent the AI from repeating a mistake I let it make in turn 5 when I ask for a refinement in turn 20?" It externalizes the model's shaky short-term memory management.