Agent design is still hard

📝 Discussion Summary (Click to expand)

The three most prevalent themes in the Hacker News discussion are:

1. Skepticism and Avoidance of Third-Party Agent Abstractions/Frameworks

Many participants advocate for building custom, lightweight agent frameworks rather than immediately adopting large, high-level frameworks. The primary concern is that these large abstractions crumble under the weight of constant LLM API evolution and ultimately constrain necessary control and customization.

Quote: "I've been building agent type stuff for a couple years now and the best thing I did was build my own framework and abstractions that I know like the back of my hand. I'd stay clear of any llm abstraction." (postalcoder)
Quote: "Some things we've[0] learned on agent design: ... Vendor lock-in is a risk, but the bigger risk is having an agent that is less capable then what a user gets out of chatgpt because you're hand rolling every aspect of your agent." (mritchie712)

2. The Importance of Low-Level Control and Custom Tooling Over General Frameworks

Users emphasize that the true difficulty in agent development lies in the specifics of managing state, tool interaction, and achieving reliable execution. Building custom foundations allows developers to manage complexity incrementally and maintain interpretability, especially given the non-deterministic nature of LLMs.

Quote: "The non-deterministic nature of LLMs already makes the performance of agents so difficult to interpret. Building agents on top of code that you cannot mentally trace through leads to so much frustration when addressing model underperformance and failure." (postalcoder)
Quote: "Have you experimented with using semantic cache on the chain of thought(what we get back from the providers anyways) and sending that to a dumb model for similar queries to “simulate” thinking?" (thierrydamiba)

3. High Rate of Technological Churn and the "Wait Calculation"

There is a strong undercurrent of recognizing that the underlying LLM technology and best practices are shifting so rapidly (e.g., context window size, function calling capabilities) that building complex systems today risks immediate obsolescence. This leads to debate over whether to build quickly or wait for foundational stability.

Quote: "My bet is that agent frameworks and platform will become more like game engines. You can spin your own engine for sure and it is fun and rewarding. But AAA studios will most likely decide to use a ready to go platform with all the batteries included." (pdp)
Quote: "What I've learned from this is that often times it is better to do absolutely nothing." (pdp, referencing building custom disk encryption shortly before AWS automated it.)

🚀 Project Ideas

Agent Abstraction Decommissioner (AAD)

Summary

A declarative tool for managing, versioning, and safely deprecating internally built LLM agent abstractions and custom interfaces over LLM APIs (OpenAI, Anthropic, etc.).
Core value proposition: Provides confidence and safety when refactoring or removing custom agent code that attempts to handle the rapid evolution of LLM APIs, addressing the pain point of "crumbling under their own weight" abstraction layers.

Details

Key	Value
Target Audience	Teams building production agents that rely on custom LLM interface wrappers or internal "agent frameworks."
Core Feature	Declaring "Abstraction Layers" with dependency mapping (which underlying LLM provider version it targets) and automated migration path suggestions based on newer, more capable underlying models (e.g., LLM API adding native Tool Calling support).
Tech Stack	Go or Rust (for high performance, cross-platform CLI/Service), YAML/JSON Schema for declarative definitions, Light integration with CI/CD pipelines.
Difficulty	Medium (Requires deep understanding of agent architecture concepts but keeps the LLM provider integration simple).
Monetization	Hobby

Notes

Why HN commenters would love it: Directly addresses postalcoder's concern: "There are so many companies with open source abstractions offering the panacea of a single interface that are crumbling under their own weight due to the sheer futility of supporting every permutation of every SDK evolution." This product manages internal crumbling abstractions.
Potential for discussion or practical utility: Could spark debate on the ROI of building bespoke frameworks vs. waiting for foundation models to integrate new patterns natively (e.g., explicit caching, structured output).

Semantic Cache Validator & Simulator

Summary

A service designed to test the effectiveness of Chain-of-Thought (CoT) semantic caching strategies before deployment to a production agent.
Core value proposition: Quantifies the reuse potential of intermediate reasoning steps, addressing the concept raised by thierrydamiba to "experiment with using semantic cache on the chain of thought."

Details

Key	Value
Target Audience	Developers implementing advanced agent logic, specifically those using complex reasoning/tool-use chaining.
Core Feature	Ingests historical agent execution logs (input, CoT trace, tool calls, final output) and simulates a new set of inputs against a configured semantic cache (vector DB). It reports hit rate, latency savings, and measures if cache hits lead to functionally identical or superior final outputs (via comparison checks).
Tech Stack	Python (for easy integration with existing prompt/trace pipelines), Sentence Transformers/Embedding Models, Redis/Weaviate for cache storage, FastAPI for serving simulation results.
Difficulty	Medium
Monetization	Hobby

Notes

Why HN commenters would love it: It turns a theoretical optimization (thierrydamiba's suggestion) into a measurable engineering metric, appealing to the technical skepticism about abstract benefits.
Potential for discussion or practical utility: Excellent utility for arguing against or justifying the complexity of implementing semantic caching for agent reasoning loops.

Proxy Layer Enforcement & Monitoring (PLEM)

Summary

A deployment-time utility and runtime monitoring tool that enforces standard behavior via an intelligent proxy layer, specifically designed to prevent model "drift" or unauthorized tool use bypassing the intended structure.
Core value proposition: Enables developers to safely rely on powerful, highly-trained APIs (like Claude Code/Agent SDK) for execution while ensuring control and observability, satisfying the "smart proxy" architecture suggested by CuriouslyC and NitpickLawyer.

Details

Key	Value
Target Audience	Teams using vendor-native agent/code execution environments (like Anthropic's Agent SDK or proprietary environments) who need external governance.
Core Feature	Intercepts all LLM I/O. It validates that tool calls match a pre-approved manifest (tool/schema) and monitors for context "leakage" or unexpected behavior (e.g., attempts to mock solutions when critical code is needed, as mentioned by `CuriouslyC`).
Tech Stack	TypeScript/Node.js (for simple HTTP proxy implementation), leveraging JSON Schema validation against tool definitions, and potentially using a specialized local LLM for cheap, fast pattern validation before forwarding to the expensive vendor API.
Difficulty	High (Requires robust error handling and maintaining parity with evolving vendor message formats).
Monetization	Hobby

Notes

Why HN commenters would love it: It validates the architectural pattern of using a proxy to insert logic (CuriouslyC: "You don't need to rewrite claude/codex, just add context engineering and tool behaviors at the proxy layer."). It solves the perceived problem of LLMs "lying" by adding a non-LLM layer of truth/enforcement.
Potential for discussion or practical utility: Could lead to community attempts to standardize proxy definitions across models/vendors.