Evaluating the Effectiveness of Automated Workflows

Evaluating LLMs is hard. Evaluating agents is even harder. This is one of the most common challenges I see when teams move from using LLMs in isolation to deploying agents that act over time, use tools, interact with APIs, and coordinate across roles. These systems make a series of decisions, not just a single prediction. As a result, success or failure depends on more than whether the final answer is correct. Despite this, many teams still rely on basic task success metrics or manual reviews. Some build internal evaluation dashboards, but most of these efforts are narrowly scoped and miss the bigger picture. Observability tools exist, but they are not enough on their own. Google’s ADK telemetry provides traces of tool use and reasoning chains. LangSmith gives structured logging for LangChain-based workflows. Frameworks like CrewAI, AutoGen, and OpenAgents expose role-specific actions and memory updates. These are helpful for debugging, but they do not tell you how well the agent performed across dimensions like coordination, learning, or adaptability. Two recent research directions offer much-needed structure. One proposes breaking down agent evaluation into behavioral components like plan quality, adaptability, and inter-agent coordination. Another argues for longitudinal tracking, focusing on how agents evolve over time, whether they drift or stabilize, and whether they generalize or forget. If you are evaluating agents today, here are the most important criteria to measure: • 𝗧𝗮𝘀𝗸 𝘀𝘂𝗰𝗰𝗲𝘀𝘀: Did the agent complete the task, and was the outcome verifiable? • 𝗣𝗹𝗮𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: Was the initial strategy reasonable and efficient? • 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Did the agent handle tool failures, retry intelligently, or escalate when needed? • 𝗠𝗲𝗺𝗼𝗿𝘆 𝘂𝘀𝗮𝗴𝗲: Was memory referenced meaningfully, or ignored? • 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 (𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀): Did agents delegate, share information, and avoid redundancy? • 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲: Did behavior remain consistent across runs or drift unpredictably? For adaptive agents or those in production, this becomes even more critical. Evaluation systems should be time-aware, tracking changes in behavior, error rates, and success patterns over time. Static accuracy alone will not explain why an agent performs well one day and fails the next. Structured evaluation is not just about dashboards. It is the foundation for improving agent design. Without clear signals, you cannot diagnose whether failure came from the LLM, the plan, the tool, or the orchestration logic. If your agents are planning, adapting, or coordinating across steps or roles, now is the time to move past simple correctness checks and build a robust, multi-dimensional evaluation framework. It is the only way to scale intelligent behavior with confidence.

You've built your AI agent... but how do you know it's not failing silently in production? Building AI agents is only the beginning. If you’re thinking of shipping agents into production without a solid evaluation loop, you’re setting yourself up for silent failures, wasted compute, and eventully broken trust. Here’s how to make your AI agents production-ready with a clear, actionable evaluation framework: 𝟭. 𝗜𝗻𝘀𝘁𝗿𝘂𝗺𝗲𝗻𝘁 𝘁𝗵𝗲 𝗥𝗼𝘂𝘁𝗲𝗿 The router is your agent’s control center. Make sure you’re logging: - Function Selection: Which skill or tool did it choose? Was it the right one for the input? - Parameter Extraction: Did it extract the correct arguments? Were they formatted and passed correctly? ✅ Action: Add logs and traces to every routing decision. Measure correctness on real queries, not just happy paths. 𝟮. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝘁𝗵𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 These are your execution blocks; API calls, RAG pipelines, code snippets, etc. You need to track: - Task Execution: Did the function run successfully? - Output Validity: Was the result accurate, complete, and usable? ✅ Action: Wrap skills with validation checks. Add fallback logic if a skill returns an invalid or incomplete response. 𝟯. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗵𝗲 𝗣𝗮𝘁𝗵 This is where most agents break down in production: taking too many steps or producing inconsistent outcomes. Track: - Step Count: How many hops did it take to get to a result? - Behavior Consistency: Does the agent respond the same way to similar inputs? ✅ Action: Set thresholds for max steps per query. Create dashboards to visualize behavior drift over time. 𝟰. 𝗗𝗲𝗳𝗶𝗻𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿 Don’t just measure token count or latency. Tie success to outcomes. Examples: - Was the support ticket resolved? - Did the agent generate correct code? - Was the user satisfied? ✅ Action: Align evaluation metrics with real business KPIs. Share them with product and ops teams. Make it measurable. Make it observable. Make it reliable. That’s how enterprises scale AI agents. Easier said than done.

Anything CAN be automated. The real question is what SHOULD be automated. Instead of going automation-crazy, there’s often space to take a step back and see if you have messy/unnecessary automations that are wasting more time than they save. Case in point: Our client in the healthcare staffing industry had set up their own Pipedrive and things were a mess. Their automated workflow fed the team tasks that were unnecessary, duplicate, or long complete. Every day was a new avalanche of 100s of “urgent” overdue tasks. Stressful, right? Our goal when working with them wasn’t to add anything new, but rather to reorganize the way they used their CRM. We dove deep into their procedures, and then… ➡️ streamlined their task list ➡️ created new KPIs …and the craziest one: ➡️ switched daily to-do lists back to manual. That’s right, no more automation for this. Team members now create their own to-do lists. It might sound odd, but that’s what this business needed to flourish. I’d like to encourage you to start thinking in this direction, too. Instead of blindly trying to automate everything possible, take a step back. Look under the hood of your business. See what your process REALLY looks like, how your automations support those workflows, and how things would be different if you tweaked them. You might need more aspects automated. You might need to get rid of some automated workflows. And you might simply need to restructure some elements of your workflow. Approach it with an open mind — you just might be surprised at what you discover. -- Hi, I’m Nathan Weill, a business process automation expert. ⚡️ At Flow Digital, we help business owners like you unlock the power of automation with customized solutions so you can run your business better, faster, and smarter. #crm #automation #business  #automationtiptuesday  #automation #workflow 

Here's what I'm seeing everywhere: AI is making teams faster, but are we making them stronger? AI is making us more productive, but are we becoming more capable? We might be able to do more, but is the ‘more’ translating to ‘more valuable’? And traditional metrics can't tell the difference. That’s why after having done over 200+ deployments, and observing the same issues, combined with recent stats that 88% of all AI projects stall/cancel/pause at POC, and MIT recently stated only 5% of GenAI projects succeed, I invented The Human Amplification Index™ (© 2025 Sol Rashidi. All rights reserved.). We need a way to measure whether AI is making our business and people more valuable or just making them busier. Here's what the product tracks: 𝟭. 𝗪𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝘁𝗵𝗲 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵 𝗼𝗳 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸𝗙𝗨𝗡𝗖𝗧𝗜𝗢𝗡™ 𝗯𝗲𝗳𝗼𝗿𝗲 𝗮𝗻𝗱 𝗮𝗳𝘁𝗲𝗿 𝗔𝗜 (© 2025 Sol Rashidi. All rights reserved.). It tells you how much of your team's time is spent on what they were actually hired to do? Most teams I assess are operating at 40-60% of their intended function. The rest? Emergency fixes, escalations, triaging, broken process workarounds, administrative busy work that has nothing to do with their core expertise. Before you implement AI, measure this baseline. Then track how AI shifts this equation. 𝟮. 𝗪𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝘁𝗵𝗲 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵 𝗼𝗳 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸𝗙𝗟𝗢𝗪™ 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗯𝗲𝗳𝗼𝗿𝗲 𝗮𝗻𝗱 𝗮𝗳𝘁𝗲𝗿 𝗔𝗜 (© 2025 Sol Rashidi. All rights reserved.) This isn't about speed, it's about friction. - How many hoops do your people jump through to complete basic tasks? - How many disconnected tools do they toggle between? - How much manual work exists because systems don't talk to each other? AI should remove friction, not just accelerate it. So build a baseline and measure how it improves with AI 𝟯. 𝗪𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝘁𝗵𝗲 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵 𝗼𝗳 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸𝗙𝗢𝗥𝗖𝗘™ 𝗯𝗲𝗳𝗼𝗿𝗲 𝗮𝗻𝗱 𝗮𝗳𝘁𝗲𝗿 𝗔𝗜 (© 2025 Sol Rashidi. All rights reserved.). When you hired each person, you saw the unique value they could bring. How much of that potential are you actually accessing? If AI is handling routine tasks but your people are still stuck in the weeds instead of contributing their highest-value thinking, you've got an amplification problem. The companies that figure this out will separate themselves dramatically from those that don't. While most leaders are asking "Are we more efficient?" The better question is: "Are our people able to contribute more of their unique human value because AI is handling everything else?" When you measure work function strength, workflow efficiency, and workforce amplification, you're measuring your true capacity for sustainable growth. That's the difference between using AI as a tool and using AI to amplify human potential. What's your experience? Are your teams becoming more capable, or just busier?

This client's "automation" actually made things worse. When Marcus first got in touch, he was pissed. Here's what happened: 6 months earlier, he'd hired a developer to automate his consulting business. Cost: $22,000 Timeline: 3 months Promise: "Fully automated client pipeline" Here's what he actually got: - A system that crashed every other week. - Automations sending wrong emails to the wrong clients. - Data spread across 7 different tools that didn’t sync at all. - His team spending MORE time fixing bugs than doing real work. The developer’s response was "It works fine on my end." The real issue was that the developer had zero business sense. He built exactly what Marcus asked for without ever understanding what Marcus really needed. Classic example of: - No user empathy - Zero future-proofing - No clue how the business actually operated Here's what we did differently: Before touching any tools or code, we spent 2 weeks deep-diving into Marcus’s business: ✅ Talked to his entire team – What headaches were they dealing with daily? ✅ Mapped every single client interaction – Where were things actually breaking down? ✅ Reviewed all the existing data – What's really happening vs. what's assumed? ✅ Narrowed it down to 3 core automations – Not 20 random features that sounded cool. We scrapped 80% of the old system and simplified it: ✔️ Easy client onboarding form that automatically generates contracts. ✔️Automated payment tracking with smart follow-ups. ✔️Project delivery workflow with clear client notifications. The impact: Client onboarding: 30 hours → 3 hours Payment collection: 45 days → 12 days Team productivity: 2X increase The takeaway: Technical skill alone is meaningless without business understanding. The best developers aren't the ones who write the most complex code, but they're the ones who know how to ask the right questions BEFORE building anything. Red flags to look out for: ❌ Developers who build exactly what you request (without challenging you). ❌ No questions about your business model or actual workflows. ❌ Big promises about "fully automating" everything. ❌ Can't clearly explain how their solution scales as you grow. Stop throwing money at automations that don't work. Follow me Luke Pierce for more content about Ai and Automation.

After helping hundreds of companies implement AI workflows, I've noticed a pattern: Success with AI depends heavily on the systems you build, not what models you use. Here's the systematic approach I've seen work time and time again: 1️⃣ Start with finding and connecting the right input data and output examples (not AI models) Most teams rush to plug in ChatGPT or Claude. But your existing data is your biggest advantage. The companies seeing 25%+ conversion lifts aren't using better AI alone. They're also feeding it better inputs. 2️⃣ Design for human-AI collaboration Your goal shouldn’t be automation but augmentation. The best implementations have clear handoffs between AI and human review. Not because AI isn't good enough but because the combination is superior. 3️⃣ Build scalable workflows (not one-off solutions) A successful AI workflow should be: → Repeatable → Customizable → Quality-focused → Data-grounded When a client needed to optimize 50,000 products, they didn't write 50,000 prompts. They built systematic workflows using AirOps that maintained quality at scale. 4️⃣ Measure what matters The metrics that matter aren't AI-specific: ● Time saved ● Quality improved ● Revenue generated ● Costs reduced Don't try to transform everything at once. Pick one high-impact workflow and perfect it. Then expand. Currently, companies getting the most from AI don’t have the biggest budgets or the best engineers. They simply approach it systematically. If you’re building something with AI, I'd love to hear what's working (or not) for your team.

Kat Shoa - great question - how do you measure the horizontal ROI (ex of AI in email, call transcription, etc)? This is such a smart distinction - and you're right that horizontal ROI is trickier to measure precisely because it's so distributed. Here's how I think about it but Brice Challamel, Greg Shove, Shruthi Shetty, Tony Gentilcore, Section or Tony Hoang may have more to add ... 𝗧𝗵𝗲 𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹 𝗥𝗢𝗜 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 When AI touches everyone's email, transcription, or document creation, the impact gets diffused across every workflow. You can't easily isolate "the AI effect" because it becomes infrastructure - like trying to measure the ROI of electricity or internet connectivity. 𝗧𝗵𝗲 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗧𝗿𝗶𝗰𝗸 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀: Create control groups. Roll out horizontal AI to Department A but not B for 90 days. Measure productivity, employee satisfaction, and output quality differences. The delta is your horizontal ROI. 𝗧𝗵𝗿𝗲𝗲-𝗟𝗮𝘆𝗲𝗿 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗟𝗮𝘆𝗲𝗿 𝟭: 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗧𝗶𝗺𝗲 𝗘𝗰𝗼𝗻𝗼𝗺𝗶𝗰𝘀 Start with the math everyone can understand: If 500 employees save 30 minutes daily on email/transcription, that's 250 hours per day. At an average wage of $50/hour, that's $12,500 daily or $3.25M annually. Simple, defensible baseline. 𝗟𝗮𝘆𝗲𝗿 𝟮: 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗲𝗿𝘀 But the real value isn't just time saved - it's what people do with freed cognitive capacity. Track: - Meeting quality scores (when transcription handles notes, do people participate more?) - Email response rates and customer satisfaction - Cross-functional collaboration frequency 𝗟𝗮𝘆𝗲𝗿 𝟯: 𝗖𝗼𝗺𝗽𝗼𝘂𝗻𝗱 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗘𝗳𝗳𝗲𝗰𝘁𝘀 This is where horizontal AI gets interesting. When everyone has better email/transcription, the entire communication system improves. Measure: - Decision speed (time from question to action) - Information cascade velocity (how fast insights spread) - Coordination overhead reduction 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 Horizontal ROI is often your biggest ROI story - but you have to measure it at the system level, not the individual level. Think platform economics, not feature economics. Would love other thoughts on above. And if needed Lanai team is happy to deep w/ folks who are working to get more data-driven on delivering measurable impact with AI tooling.

An AI agent can get the 𝗿𝗶𝗴𝗵𝘁 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲... but take a 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆 𝗶𝗹𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗽𝗮𝘁𝗵 to get there. Could you trust an AI agent if you didn’t know how it made decisions? That’s why evaluating both the output and the trajectory is critical. Google just launched Vertex AI Gen AI Evaluation (public preview), a tool that helps you: 🛠️ Evaluate any generative AI model or application 📊 Benchmark results against your own criteria 🔍 Analyze not just the response, but how the AI got there How does it work? 1. Final Response Evaluation – Did the AI agent achieve the expected outcome? 2. Trajectory Evaluation – Did it take the correct, efficient, and logical steps to get there? With 𝘀𝗶𝘅 𝗸𝗲𝘆 𝘁𝗿𝗮𝗷𝗲𝗰𝘁𝗼𝗿𝘆 𝗺𝗲𝘁𝗿𝗶𝗰𝘀, you can analyze: Exact Match – Did the AI follow the ideal decision path? In-Order & Any-Order Match – Did it complete the right steps, in the right order? Precision & Recall – Did it take relevant actions and avoid unnecessary ones? Single-Tool Use – Did it use the correct tools when needed? This means you can benchmark any AI agent or generative model against your own criteria based on these metrics. Because if your AI is taking the scenic route, 𝘆𝗼𝘂’𝗿𝗲 𝗽𝗮𝘆𝗶𝗻𝗴 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗴𝗮𝘀. How do you currently measure AI performance in your business workflows? #AI #AIAgents #GenAI #AIEvaluation For an example of how this works in action (LangGraph customer support agent evaluation), link in the comments. ⬇️