Deflect Tickets with AI to Reduce Overhead Costs

A sprinkler company had a problem most businesses quietly ignore. Peak season hit. Support queries exploded past one million. Their small team had no way to keep up. So they did one thing differently. They automated their entire customer support query system using AI agents. Year one result: $928,750 saved in a single year. No extra hires. No new office. No overnight transformation. Now I am not a big fan of voice agents personally. Something about them still feels off. But getting the right information to a customer at 11pm on a Sunday when your team is offline? That is not replaceable. That is just good business. The part that gets me every time with these stories is the same. It was never about the technology. It was about finally deciding which problem to fix first. (Source: Crescendo.ai, Rachio Case Study 2024)

Three agents. One task. Five identical messages sent. A builder this week discovered his multi-agent system was calling the same tool three times simultaneously. No error. No warning. Three duplicate actions, each confident it was the only one running. He spent a day debugging code that was technically correct. The problem wasn't the code. It was the gap between what the agents decided to do and what actually happened when they did it. Nobody was watching that gap. This is the most common production failure I see right now. Not crashes. Not errors. Quiet duplication. Quiet misfires. Your agent completes the task and breaks something at the same time. 88% of AI agents never reach production. This is a big reason why. The fix isn't smarter agents. It's a layer that watches what agents do at the moment they do it, catches the overlap, and stops the duplicate before it fires. If you're building multi-agent systems today, add one check before any tool executes: has this exact call already been dispatched in this session? One guard. Massive difference. Have you ever caught your agent doing something you didn't realize it was capable of breaking?

Don't move agents to human interfaces, move interfaces to agents. If you are automating, you will save time and money. Fewer steps and easier to understand (by both human and agent). Imagine verifying updating excel at "bot" speed. Sure, its neat looking, but humans are just "in the way" (not "in the loop"). If you must "be human", make tools that compress time (speed up and down). Then you have a chance to be helpful!

Most projects don't need an agent. They need a function call with retries. Five questions I ask before building one. Agent hype is real and useful, but I've shipped enough of them to know when they're not the answer. 1. Does this task have variable steps? → If the steps are fixed, you don't need an agent. You need a state machine with LLM-powered nodes. 2. Does the model need to choose between tools? → If there's only one possible tool to use, just call it. Skip the agent loop. 3. Is non-determinism acceptable here? → Agents drift. If your task needs the same answer every time given the same input, agents are the wrong shape. 4. Can I afford 5–20× the inference cost? → Single-shot prompts: 1 LLM call. Agents: 5–20 calls per task. Make sure the value justifies the cost. 5. Do I have evals that can catch silent regressions? → Without evals, agents will quietly degrade as you change prompts, and you'll find out from users. If 4 out of 5 are "yes," build the agent. Otherwise, keep it simple. A retry loop, structured output, and one tool call solve more problems than people admit. This is the post that should have come first in this series. Three weeks in; what would you add to this list?

You’re not competing against agents anymore. You’re competing against: AI-enabled teams automated pipelines response-time systems So the real question isn’t: “How many agents do I have?” It’s: “How many transactions can my system absorb per agent?”

"The agent's output is only as good as its reasoning and context." This sounds obvious. But most teams optimize the wrong side. They focus on: → Better models (more parameters, newer versions) → More sophisticated prompts (longer, more detailed) → More tools (broader capabilities) They neglect: → What data actually enters the context window → How much noise vs signal is in each LLM call → Whether the agent's knowledge and policy are clearly separated The quality chain: Input quality → Context quality → Reasoning quality → Output quality If any link is weak, everything downstream suffers. You can't fix bad context with a better model. You can't fix missing knowledge with a longer prompt. You can't fix unclear policy with more tools. Start with the context. Everything else follows. I wrote a free 27-page technical guide covering all of this in depth. DM me "AGENT" and I'll send you the link. #ContextEngineering #AgenticAI #AIAgents

Agents have neither memory you should fully trust nor consequences they can personally own. The safest and most useful agent systems are built around narrow tools, explicit contracts, and constrained write scopes. your interface budget decides what the agent can observe, what it can change, how far the change can propagate, and how quickly a human can reverse it. https://lnkd.in/gs4uUaJA

We've spent two years arguing about which model is smarter. But the real enterprise battle is about to start elsewhere: the agent control plane. Anthropic's recent move into orchestration shows they aren't content being just another API. They want to own the environment where agents actually execute tasks, manage memory, and use tools. This is a massive shift for three groups: 1. Builders: Your moat isn't your prompt; it's how deeply you integrate into the customer's operational workflow. 2. Investors: Look past the model benchmarks. The real value is in the sticky infrastructure that makes switching providers a nightmare. 3. Operators: Security and governance are no longer nice-to-haves. If your agent can execute code, you need a robust identity and permissioning layer. The era of chatbot-as-a-service is ending. We are moving into the era of agentic-infrastructure-as-a-service. Is your roadmap prioritizing model intelligence or agentic control? Read the full launch intelligence: https://lnkd.in/gh3Xnmhu

5 guardrails so your agents don't destroy your business. An AI agent destroyed a small business in 9 seconds. The guardrails were there. It guessed anyway. 👇 What they mean in practice: 1. Require human approval for every irreversible action. Not in the prompt. Not in the rules file. At the infra layer. Outside the agent's loop. 2. Version control everything before the agent touches it. Commit before every session. A wrong guess needs a rollback point. 3. Separate accounts/IAM roles for staging and prod. A naming convention isn't a boundary. Make it structurally impossible to cross. 4. Scope every token to the minimum required. A token created for one task shouldn't reach everything else. 5. Default to minimum MCP tools. Document why if you need more. Every connected tool is a blast radius. Own the decision. Soft guardrails inside the model's reasoning loop are not a safety architecture. Safety lives at the IAM layer, the network boundary, and the human approval gate. Outside the agent. Not inside it. A real business destroyed in 9 seconds that could have been avoided. 🔗 Full thread: https://lnkd.in/eCh85V2Z #AgenticLeader #AIEngineering #SoftwareArchitecture #TechLeadership #AIAdoption

Having a very frustrating experience with SimpliSafe that highlights what can happen when switching costs are high and company operations stray away from consumer-centric service. My home was recently inspected by the city. The city fire marshal requires more information about my SimpliSafe system to determine if it meets local fire code. If I can't provide this information, I have to replace the system. Given the regulated nature of fire monitoring systems, this should be a straightforward ask. The devices are marked with UL certifications. Note that the monitoring certificate was not sufficient - the fire marshal wants operating and device specs. SimpliSafe's customer support process is pretty standard - text or call; start with a bot, escalate to a human. However, seven attempts later, I'm still empty-handed. Text attempts (n=3) have all resulted in dead ends ("We don't have the option to do that"; call this number). Voice calls (n=4) have been worse. After confirming that the info from the website (a very basic 3-5 paragraph explanation of how alarm monitoring works) and my alarm cert aren't sufficient, I'm put on hold and then *disconnected*. Yes - I've been disconnected on 3 out of the 4 calls. Pondering next steps, but thought I'd share with my network. In the age of AI replacing humans and financial pressure on company operations, it can be easy to neglect customer service.