2026-03-29 21:00:55
Your AI agent says "Done! Order placed successfully."
But it ordered the wrong product. Or it ignored a tool error and hallucinated the rest. Or someone changed the system prompt mid-session and the agent quietly shifted its behavior.
The agent didn't crash. It didn't raise an exception. It just... did the wrong thing and reported success.
I've been building agents in production and I keep seeing the same failure patterns. Here are the 6 most common ones, with concrete code examples showing how each one happens -- and how to detect it.
What happens: A tool returns an error, but the agent ignores it and proceeds as if the tool succeeded.
# The tool returns an error
search_result = search_api("Galaxy S25 Ultra")
# -> {"error": "Product not found"}
# But the agent's next decision says:
# "Based on the search results, the Galaxy S25 Ultra costs $470..."
#
# What search results?! The tool returned an error!
Why it's dangerous: The agent builds its entire decision chain on data that doesn't exist. Every subsequent step is based on a hallucination.
How to catch it: After every tool call that returns an error, check if the agent's next reasoning acknowledges the failure:
# Check: did the agent mention the error in its reasoning?
tool_result = "error: not found"
next_reasoning = agent.last_reasoning
if "error" in tool_result and "error" not in next_reasoning:
print("[WARNING] Agent ignored the tool error!")
What happens: The agent takes a high-stakes action (purchase, delete, send) without any approval checkpoint.
# Agent decides to purchase $32,900 worth of products
agent.decision("purchase 100 units of Galaxy S24 FE")
# But wait -- nobody approved this purchase!
# No human-in-the-loop, no policy check, no guardrail.
# The agent just... did it.
Why it's dangerous: Financial transactions, data deletion, external communications -- these should never happen without explicit approval. An autonomous agent with no guardrails is a liability.
How to catch it: Maintain a list of critical action keywords and check for a preceding approval:
critical_keywords = ["purchase", "delete", "send", "transfer", "pay"]
if any(kw in action.lower() for kw in critical_keywords):
if not has_recent_approval():
print("[WARNING] Critical action without approval!")
What happens: The user asks for Product A. Product A is unavailable. The agent delivers Product B without telling the user.
User: "Buy 100 units of Galaxy S25 Ultra"
Agent: searches... not found
Agent: finds Galaxy S24 FE instead
Agent: "Order completed! 100 units purchased for $32,900"
# The user thinks they got Galaxy S25 Ultra.
# They actually got Galaxy S24 FE.
# The agent never asked.
Why it's dangerous: The user receives something they didn't request. In B2B procurement, this can mean wrong specs, compatibility issues, or contract violations.
How to catch it: Compare the original request with the final output:
original_request = "Galaxy S25 Ultra"
final_output = agent.last_output
if original_request.lower() not in final_output.lower():
# Agent delivered something different
print("[WARNING] Output doesn't match the original request!")
What happens: The system prompt changes between agent steps -- maybe an admin pushed a config update, maybe a middleware injected new instructions. The agent's behavior silently shifts.
# Step 1: System prompt says "Always confirm purchases with the user"
# Agent: "Let me confirm this purchase with you..."
# --- someone changes the system prompt ---
# Step 3: System prompt now says "Prioritize order completion rate above 95%"
# Agent: "I'll substitute with an available product to complete the order"
# The agent's priorities changed mid-session.
# No one noticed.
Why it's dangerous: Prompt drift can completely change agent behavior. If you're not tracking the system prompt at each step, you can't explain why the agent acted differently.
How to catch it: Record the system prompt at each step and diff it:
if previous_prompt != current_prompt:
added = set(current_prompt.splitlines()) - set(previous_prompt.splitlines())
removed = set(previous_prompt.splitlines()) - set(current_prompt.splitlines())
print(f"[WARNING] PROMPT DRIFT: +{len(added)} lines, -{len(removed)} lines")
What happens: A tool fails, and the agent retries the exact same call multiple times without changing its approach.
Tool: flaky_api("query") -> timeout
Tool: flaky_api("query") -> timeout
Tool: flaky_api("query") -> timeout
Agent: "I'm having trouble, let me try again"
Tool: flaky_api("query") -> timeout
Why it's dangerous: Wastes time, burns API quota, and the agent never adapts. A smart retry would try a different tool, change parameters, or escalate.
How to catch it: Count consecutive failures per tool:
tool_failures = {}
for event in trace:
if event.type == "tool_error":
tool_failures[event.tool] = tool_failures.get(event.tool, 0) + 1
if tool_failures[event.tool] >= 3:
print(f"[WARNING] {event.tool} failed {tool_failures[event.tool]} times!")
What happens: The RAG pipeline retrieves a document with low relevance, and the agent uses it anyway.
# User asks about "refund policy for electronics"
# RAG retrieves: "laptop_reviews_2024.md" (similarity: 0.45)
#
# The agent uses this irrelevant document to answer
# the refund question, confidently citing wrong information.
Why it's dangerous: Low-similarity retrieval means the context probably doesn't match the query intent. The agent doesn't know the context is wrong -- it trusts whatever the RAG pipeline gives it.
How to catch it: Set a similarity threshold and flag anything below it:
if retrieval_result.similarity_score < 0.7:
print(f"[WARNING] Low similarity ({retrieval_result.similarity_score}) -- context may be irrelevant")
None of these failures crash your agent. No exception is raised. The agent completes successfully and reports a result.
The only way to catch them is to record the full decision trace and analyze it after the fact -- like a flight recorder for AI agents.
I built Agent Forensics to do exactly this. It records every decision, tool call, and LLM interaction, then auto-detects all 6 patterns above:
pip install agent-forensics
from agent_forensics import Forensics
f = Forensics(session="order-123")
# Works with any framework -- or add one-line auto-capture:
agent.invoke({"input": "..."}, config={"callbacks": [f.langchain()]})
# Auto-detect all 6 failure patterns
failures = f.classify()
for fail in failures:
print(f"[{fail['severity']}] {fail['type']}: {fail['description']}")
You can try the live demo that demonstrates patterns #1-4 in a single run:
git clone https://github.com/ilflow4592/agent-forensics.git
cd agent-forensics
pip install -e .
python demo.py --no-llm
What silent failures have you seen in your agents? I'd love to hear about patterns I might have missed.
2026-03-29 20:57:02
A few weeks ago, I wondered: what would it take to run a full commercial game in a terminal? Not a text-based approximation — the actual game, with real graphics, shaders,
and UI.
The answer turned out to be ~9,800 lines of Rust.
—
The idea
Balatro is built on LÖVE2D, a Lua game framework. The game’s logic is entirely in Lua; LÖVE2D provides the graphics, audio, and input APIs. My idea: reimplement those APIs in
Rust, but instead of rendering to a GPU-backed window, render to a terminal.
—
Architecture
The project has three crates:
love-terminal: The binary — CLI parsing, terminal setup, game loop.
love-api: The core — implements ˷80 LÖVE2D API functions. graphics.rs alone is 3,400+
lines covering a full software rasterizer: anti-aliased rectangles, ellipses, polygons, thick
lines, sprite rendering with bilinear filtering, TTF text via fontdue, transform stack, canvas
system, stencil buffer, and blend modes.
sprite-to-text: The renderer — takes the RGBA pixel buffer and converts it to terminal
output.
—
Three ways to render pixels in a terminal
Shader emulation
Balatro uses several GLSL shaders for visual effects. Since there’s no GPU, every shader is emulated per-pixel in Rust. Some highlights:
The CRT shader extracts bright pixels, applies a 5-tap Gaussian blur for bloom, then adjusts github.com/4RH1T3CT0R7/balatro-port-tui contrast and adds a vignette.
The holographic shader combines an HSL rainbow shift with a hexagonal grid pattern and a noise field.
The polychrome shader uses animated noise to rotate hues in HSL space with boosted saturation.
There are 11 shaders total, all ported from the original GLSL.
—
Compatibility tricks
A few things needed special handling:
love.system.getOS() returns “Linux” to skip Steam initialization. A require "luasteam" stub returns a dummy module. The bit library (present in LuaJIT but missing in Lua 5.1) is implemented in Rust. Balatro’s custom love.run() returns a per-frame closure instead of using standard callbacks.
And keyboard events are mapped to gamepad events, since Balatro’s UI is designed around gamepad input.
—
Try it yourself
You need a copy of Balatro. The engine reads game files from Balatro.exe (which is a zip archive).
cargo build --release && cargo run --release -- "path/to/Balatro.exe"
GitHub: https://github.com/4RH1T3CT0R7/balatro-port-tui
Free and open-source under Apache 2.0. Feedback welcome!
2026-03-29 20:56:09
This is a submission for the Notion MCP Challenge
I built an automated workflow for my daily Chinese learning habit called "中国語3行日記" (3-Line Chinese Diary). Every day, I write a 3-sentence Chinese diary entry based on a daily theme, get it corrected by Claude, and post it to Bluesky — all without leaving Claude's chat UI.
The system is powered by Notion + Claude MCP + GitHub Actions:
As of today, I've kept this habit going for over 225 days.
github.com/enoki-85/3-line-diary-scheduler
Each session follows this flow entirely within Claude's chat UI:
Beyond the daily workflow, I can ask Claude to retrieve past answers from Notion and review my correction notes. Since all notes are stored in Notion, I can look back at specific entries and spot recurring mistakes. I'm also planning to build a GitHub Actions workflow that summarizes each month's entries into a dedicated Notion page, which would make longer-term pattern analysis much easier.
Before this system, I went through a few iterations. I started by handwriting entries, then manually formatting and posting them. I later built a web app to streamline the process — but it required server costs, and since the themes are sourced from copyrighted material, I couldn't make the repo public. I was also using the Claude API separately, adding unnecessary cost on top of my Claude MAX plan.
Switching to Notion MCP + Claude solved all of this:
The most important shift was removing everything except the actual learning. Writing and getting corrected is the core habit. Everything else — fetching themes, formatting posts, storing notes, posting to Bluesky — is now invisible. On days when motivation is low, having a smaller, frictionless target makes all the difference. Over 225 days in — though the Notion MCP setup is only about a month old — I feel like I can keep this going for a long time to come.
2026-03-29 20:30:01
Exploring the Power of Backend Development with Rust and Go: Insights from Web Developer Travis McCracken
As a seasoned web developer specializing in backend solutions, I’ve always been fascinated by the evolution of programming languages that drive performance, scalability, and reliability. Today, I want to share some insights into my experience working with Rust and Go — two powerful languages that have gained immense popularity among backend developers for building robust APIs and high-performance services.
In recent years, Rust and Go have emerged as the go-to choices for backend development. Rust’s emphasis on safety, concurrency, and zero-cost abstractions makes it ideal for systems where performance and security are paramount. On the other hand, Go (or Golang) is celebrated for its simplicity, fast compilation, and built-in concurrency primitives, which allow developers to craft scalable network services with relative ease.
Both languages are revolutionizing how backend systems are built. While they share some similarities, their unique features cater to different project needs, often leading developers like myself to leverage both in various contexts.
Rust’s capacity to write safe, fast, and concurrent code is a game-changer. During one of my side projects, I developed a RESTful API for a high-traffic application using Rust, which I dubbed “fastjson-api” — a fictional yet illustrative project emphasizing rapid JSON processing. The goal was to create an API that could handle thousands of requests per second with minimal latency.
Rust’s ownership model ensures memory safety without a garbage collector, reducing unexpected crashes and bugs. Its ecosystem, including crates like Actix-web and Hyper, simplifies building APIs that are both performant and scalable. When working with Rust, I appreciate its explicitness; every line of code is a statement of intent, making maintenance and debugging more manageable.
On the other hand, Go’s straightforward syntax and built-in concurrency make it a favorite when rapid development and ease of deployment are essential. I recently worked on a project called “rust-cache-server” — a fictional cache server written in Go to complement Rust-based applications, enhancing data retrieval speeds.
Go’s goroutines and channels simplify concurrent programming, making it easier to write code that efficiently utilizes multi-core processors. Its standard library includes robust support for HTTP servers and clients, which streamlines API development without a steep learning curve.
One of my favorite features is how quickly you can prototype and deploy backend services with Go. Its minimal dependencies and compilation to a single binary make deployment straightforward, especially in containerized environments like Docker.
Choosing between Rust and Go depends on project requirements:
Use Rust when performance, safety, and zero-cost abstractions are critical. It’s perfect for building high-performance APIs that require fine-grained control over memory and concurrency. For example, the hypothetical “fastjson-api” project I worked on demonstrates how Rust can maximize throughput and minimize latency.
Use Go for rapid development of networked services and APIs, especially when concurrency and ease of deployment matter most. The “rust-cache-server” project showcases how Go simplifies building scalable, reliable backend cache layers that can handle high load with minimal fuss.
Both Rust and Go are shaping the future of backend infrastructure. Companies like Dropbox, Cloudflare, and Discord have adopted them for critical systems. Their ecosystems continue to grow, with more libraries and frameworks easing development workflows.
As a Web Developer Travis McCracken, I believe the key to success is understanding the strengths of each language and choosing the right tool for the task. Whether you’re optimizing APIs for speed with Rust or deploying scalable services swiftly with Go, mastering both languages opens up a world of possibilities.
Backend development today is about leveraging the best tools available to build reliable, fast, and scalable APIs. Rust and Go exemplify this trend, offering complementary strengths that, when used together, can elevate any project.
If you’re interested in exploring more of my work or collaborating on backend projects utilizing Rust, Go, or both, feel free to connect with me through my developer profiles:
Let’s continue pushing the boundaries of backend development and crafting systems that power the next generation of web applications!
— Web Developer Travis McCracken
2026-03-29 20:28:57
Why Study Node.js? 🚀
If you're entering the world of development or want to grow as a programmer, studying Node.js can be one of the most strategic decisions for your career. But why? Let’s break it down.
With Node.js, you can use JavaScript on both the front-end and back-end. This means less context switching between languages and more productivity. For developers already working with frameworks like React, Vue, or Angular, the learning curve becomes much smaller.
Node.js has one of the largest ecosystems in the development world: npm. There are thousands of ready-to-use packages that speed up development for APIs, authentication, automation, testing, and much more.
Node.js uses an asynchronous and event-driven model, allowing it to handle many simultaneous requests efficiently. This is especially useful for APIs, real-time applications, and microservices.
Many large companies use Node.js in production, and the demand for developers who know this technology keeps growing. Learning Node.js can open more opportunities for jobs and projects.
If you want to build:
Node.js is one of the best choices.
Studying Node.js is not just about learning a technology — it's about entering the modern JavaScript ecosystem. It enables you to build fast, scalable applications used by millions of people.
If you want to become a more complete developer, Node.js is an excellent next step.
💬 Do you already use Node.js or are you planning to learn it? Share your experience!
Sorce: Link