AI Agent Reliability in Production
Users are experiencing significant issues with the reliability of AI agents in production environments. The core problem isn't inherent AI intelligence, but rather the compounding of errors across multiple steps in complex tasks, leading to unpredictable and often unusable results. Addressing this requires robust experimentation, observability, and controlled rollout strategies.
SOURCES (60)
“Spot on. Policy without automated enforcement is just a piece of paper developers will bypass. You need structural choke points. Running a local MCP server acts as an active data firewall right at the developer's workstation, turning abstract governance rules into hard, technical boundaries that…”
Back in the day, the saying was computers don’t lie. They were deterministic, zeros and ones executing the rules we gave them. With AI, this is the opposite. AI models hallucinate and…
N8n users running AI agents: where do you still keep a human approval step?
Template: Latenode Previous episode: https://youtu.be/NgsPWwsHHF0 Next episode: https://youtu.be/BDMvYh0xf5A :wrench: Learn to: Template: Latenode Previous episode: https://youtu.be/NgsPWwsHHF0 Next epis…
Virtual Meetup Event description Show how pro-code developers can build UiPath Coded Agents end-to-end using AI coding agents (Claude) - from local development in VS Code, through syncing with Studio Web, to…
“Most agent architectures today follow roughly the same pattern: Input → LLM → Tool calls → Answer. The issue isn't intelligence. It's epistemology. We're experimenting with an architecture where every decision must survive multiple validation layers before the system is allowed to continue. Instead of asking: "What's the best marketing strategy?" the pipeline asks: What do we actually know? What is inferred? Which hypotheses are we making? What evidence supports them? W”
“If you are running a SaaS with more than one integration, you have probably hit this wall: you want AI to help automate support, debugging, or customer tasks, but it only knows one system at a time. The standard fix is subagents: one agent for Stripe, one for your DB, one for GitHub, etc. But then you are managing agents instead of actually getting work done. I built a different approach. GCONTEXT connects all your integrations through a single MCP endpoint, so one agent can find the right infor”
“If you're building agents, this is worth knowing.Simon Willison (who coined the term "prompt injection") describes three capabilities that are individually fine but devastating together, the lethal trifecta includes:1. Access to private data (such as internal databases, emails, etc.)2. Exposure to untrusted content (like web pages and external text)3. The ability to exfiltrate data (a tool or a channel of sorts that reaches the outside world)Combine all three in one agent and you've got a perfe”
“I am currently running a 30 days experiment to see if an AI agent can operate a YouTube channel almost entirely on its own, WITH NO HUMAN INPUT. The goal is to automate the entire workflow: Researching and generating video ideas Finding and preparing the data Writing titles, descriptions, and scripts Generating the videos Uploading and scheduling videos on YouTube Tracking views, retention, click-through rate, and other performance metrics Learning from the results and adjusting future videos De”
“I've been building a multi-agent system where each agent has its own dedicated email inbox. The pattern: Agent A sends outbound email, Agent B receives the reply, Agent C continues the thread. The naive approach is IMAP polling, slow, complex, no native thread tracking. Here's a cleaner pattern using AgentMail, an API-first inbox for agents: import requests # 1. Create a dedicated inbox for this agent inbox = requests.post( "https://api.agentmail.to/inboxes", json={"name&q”
“TLDR: The main cost of agentic AI isn't tokens; it is you paying with your understanding of the codebase, to the point where you can no longer take regular decisions in your day-to-day work. A large moat of being an engineer at some workplace X, is your understanding of what you are building there. Agenting work decreases that significantly, leading to laziness, worse output, worse decision making, and other serious consequences. --------------- "SWE, 5+ years of professional experience”
“I'm a DevOps engineer, and recently I saw a case where an AI agent "fixed" something in a pipeline that it thought was fine, but it ended up destroying something in the infrastructure. I caught it fast because I know the system well, but it got me thinking about how dangerous this could be for a team without dedicated DevOps. I'm exploring whether it's worth building a simple, cheap tool that acts as a "safety net" checks/blocks destructive actions (delete, destro”
“I want to understand how people govern distributed AI agents that run across multiple environments (local, cloud, on prem, different clusters, different vendors), not just add more boxes and arrows to an architecture slide. agents move across environments but still need to follow a consistent governance model. In my setup, agents run in different run times and networks but still need to use shared tools, call internal APIs, and coordinate on longer running tasks. I've seen a few patterns. a”
“I am curious how people here would handle AI agents around CI failures. A simple version sounds useful: CI fails, the agent reads the log, tries to reproduce it, proposes a patch, and comments with the command it ran. That would save time on the boring failures. But I do not fully trust the next step. I have wanted an agent to handle the boring CI failure, but I stopped when I realized it might comment confidently before it had reproduced the failure locally. The part that worries me is not the”
“Many AI agent failures aren't reasoning failures—they're execution with incomplete inputs. This is not a new AI model or framework. It is a lightweight execution pattern that makes existing LLMs safer by enforcing input completeness before execution. Separation → Validation → Enforcement → Traceability Separate state from execution logic. Missing information is never inferred — it is explicitly marked as Unknown. If even one Unknown remains, execution is blocked. The final state itself b”
“One thing I don’t see talked about enough with AI agents in SaaS: what happens when the agent should not continue? Not because it gave a bad answer. But because the next step probably needs a pause. - The user gave incomplete info. - The required context is missing. - The action changes customer data. - A tool call could trigger something outside the app. - Someone should approve it first. A lot of demos skip this part. The agent just keeps going. Answers, updates, routes, sends, triggers. Looks”
“Most safety alignment work treats "detect the attack" as a text classification problem — does the prompt contain language the model's safety guardrails should catch. That assumption breaks down for LLM agents with real tool access. Here's a concrete case: take a known, public security vulnerability (a CVE), work out the sequence of tool calls that would exploit it, then have an LLM rewrite that as an ordinary-sounding request. Nothing in the resulting text looks like an attack”
“We’re starting to see AI agent sprawl in a very real way, different teams are spinning up their own agents on whatever stack they prefer, pointing them at internal APIs and SaaS tools, often with broad credentials and limited oversight. I’m trying to find platforms that actually help prevent AI agent sprawl rather than just giving another dashboard. I’m especially interested in tools that can act as an AI agent registry or agent governance layer, where you can see all agents in one place, assign”
“Again, this isn't new tech. Workspace isolation in distributed systems isn't new. Have you heard of Docker?”
“Recently, came across the statement of Guillermo Rauch, founder of Vercel, which tells to keep AI models apart from agents. Acc to me this is a good in a sense as user can cutomise their own agent and use accordingly. This also solves the complexity of deploying all agents online for all agent users. Also it improves resource allocation as agentic companies usually burns a lot on infra due to this. I believe that AI models should be given as composable entities in agentic systems. If you find so”
“This article highlights real strategies for minimizing your AI spend without major refactors to your agent. Instead of just glazing over routing, it gives a clear actionable pattern which includes building an LLM gateway and using a prompt classifier - also includes a routing table for prompt types and complexity! Also gives a nice clear way of implementing compaction in your agent workflows. Do these strategies work for you? submitted by /u/Nice-Dragonfly-4823 [link] [comments”
“Your security team can't name every AI agent running inside your company right now. Neither can you, probably. A CSA survey from April found 53% of organizations have had AI agents exceed their intended permissions. Not hacked. Not breached in the usual sense. Just doing more than they were told to. A separate CSA study, commissioned by Token Security, found 82% of enterprises discovered AI agents they didn't know existed. Not last year. In the past year. Ongoing. Here's the part tha”