Optimizing AI Email Agent Performance

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.

I discovered I was designing my AI tools backwards. Here’s an example. This was my newsletter processing chain : reading emails, calling a newsletter processor, extracting companies, & then adding them to the CRM. This involved four different steps, costing $3.69 for every thousand newsletters processed. Before: Newsletter Processing Chain (first image) Then I created a unified newsletter tool which combined everything using the Google Agent Development Kit, Google’s framework for building production grade AI agent tools : (second image) Why is the unified newsletter tool more complicated? It includes multiple actions in a single interface (process, search, extract, validate), implements state management that tracks usage patterns & caches results, has rate limiting built in, & produces structured JSON outputs with metadata instead of plain text. But here’s the counterintuitive part : despite being more complex internally, the unified tool is simpler for the LLM to use because it provides consistent, structured outputs that are easier to parse, even though those outputs are longer. To understand the impact, we ran tests of 30 iterations per test scenario. The results show the impact of the new architecture : (third image) We were able to reduce tokens by 41% (p=0.01, statistically significant), which translated linearly into cost savings. The success rate improved by 8% (p=0.03), & we were able to hit the cache 30% of the time, which is another cost savings. While individual tools produced shorter, “cleaner” responses, they forced the LLM to work harder parsing inconsistent formats. Structured, comprehensive outputs from unified tools enabled more efficient LLM processing, despite being longer. My workflow relied on dozens of specialized Ruby tools for email, research, & task management. Each tool had its own interface, error handling, & output format. By rolling them up into meta tools, the ultimate performance is better, & there’s tremendous cost savings. You can find the complete architecture on GitHub.

I burned $47 in API calls watching an agent check the same API endpoint every 30 seconds for 6 hours straight. It was supposed to monitor a deployment status. Instead, it just… kept checking. No breaks. No strategy. Just pure, expensive anxiety. That's the problem with current AI agents: they're brilliant at one-time tasks but absolutely terrible at waiting. Microsoft Research just released something that fixes this: SentinelStep. It's now open-sourced in their Magentic-UI system, and honestly, this changes how we think about agent workflows. Here's what makes it work: The system breaks monitoring into three components: actions (what to check), conditions (when to stop), and polling intervals (how often to check). Simple concept, but the execution is clever. Dynamic polling is where it gets interesting. The agent doesn't blindly check every minute. It makes an educated guess based on task urgency. Monitoring quarterly earnings? Less frequent checks. Tracking an urgent email? More aggressive polling. Then it adjusts based on observed patterns. Now, here's my take on what's probably happening behind the scenes: The system likely maintains a state snapshot after the first check, basically freezing what the agent knows at that moment. Think of it like taking a photo of the agent's brain. For each subsequent check, instead of carrying forward the entire conversation history (which would expand the context window), it loads the frozen snapshot, performs the new check, compares the results, and determines whether the condition is met. The polling adjustment probably uses something straightforward maybe exponential backoff with task-specific multipliers. If nothing changes after a few checks, wait longer next time. If patterns emerge (like "emails usually arrive between 9-11 AM"), the interval shrinks during those windows. No fancy ML needed, just sensible heuristics. Context management is the real win here. Without it, a 2-day monitoring task would accumulate thousands of tokens of redundant checks. With state snapshots, each check stays isolated and lightweight. They tested it with SentinelBench and showed success rates jumping from 5.6% to 33-39% for 1-2 hour tasks. But here's what I think matters more than those numbers: where you'd actually use this. Imagine monitoring CI/CD pipelines that take hours to complete, tracking competitor pricing that updates sporadically, or watching for specific social media mentions across days. These aren't hypothetical—they're tasks we currently handle with clunky cron jobs or manual checking. pip install magentic-ui right now and start experimenting. The foundation is solid, though you'll want to test thoroughly for production use cases (Microsoft's transparency note calls this out explicitly). This feels like one of those unglamorous infrastructure pieces that quietly enable a whole new category of automation. Not flashy, but exactly what we needed.

LangChain recently published a helpful step-by-step guide on building AI agents. 🔗 How to Build an Agent –https://lnkd.in/dKKjw6Ju It covers key phases: 1. Defining realistic tasks 2. Documenting a standard operating procedure 3. Building an MVP with prompt engineering 4. Connect & Orchestrate 5. Test & Iterate 6. Deploy, Scale, and Refine While the structure is solid, one important dimension that’s often overlooked in agent design is: efficiency at scale. This is where Lean Agentic AI becomes critical—focusing on managing cost, carbon, and complexity from the very beginning. Let’s take a few examples from the blog and view them through a lean lens: 🔍 Task Definition ➡️ If the goal is to extract structured data from invoices, a lightweight OCR + regex or deterministic parser may outperform a full LLM agent in both speed and emissions. Lean principle: Use agents only when dynamic reasoning is truly required—avoid using LLMs for tasks better handled by existing rule-based or heuristic methods 📋 Operating Procedures ➡️ For a customer support agent, identify which inquiries require LLM reasoning (e.g., nuanced refund requests) and which can be resolved using static knowledge bases or templates. Lean principle: Separate deterministic steps from open-ended reasoning early to reduce unnecessary model calls. 🤖 Prompt MVP ➡️ For a lead qualification agent, use a smaller model to classify lead intent before escalating to a larger model for personalized messaging. Lean principle: Choose the best-fit model for each subtask. Optimize prompt structure and token length to reduce waste. 🔗 Tool & Data Integration ➡️ If your agent fetches the same documentation repeatedly, cache results or embed references instead of hitting APIs each time. Lean principle: Reduce external tool calls through caching, and design retry logic with strict limits and fallbacks to avoid silent loops. 🧪 Testing & Iteration ➡️ A multi-step agent performing web search, summarization, and response generation can silently grow in cost. Lean principle: Measure more than output accuracy—track retry count, token usage, latency, and API calls to uncover hidden inefficiencies. 🚀 Deployment ➡️ In a production agent, passing the entire conversation history or full documents into the model for every turn increases token usage and latency—often with diminishing returns. Lean principle: Use summarization, context distillation, or selective memory to trim inputs. Only pass what’s essential for the model to reason, respond, or act.. Lean Agentic AI is a design philosophy that brings sustainability, efficiency, and control to agent development—by treating cost, carbon, and complexity as first-class concerns. For more details, visit 👉 https://leanagenticai.com/ #AgenticAI #LeanAI #LangChain #SustainableAI #LLMOps #FinOpsAI #AIEngineering #ModelEfficiency #ToolCaching #CarbonAwareAI LangChain

🚨 AI Emails From the Full Signal Chain live in Warmly! Problem: Email sequences aren't personalized with signals Solution: Trigger AI emails to prospects if your Inbound Agent fails to convert or based on any signal. Backstory: A few months ago I pulled our own inbound report and watched 4 out of 5 qualified hand-raisers die in a generic nurture sequence written three quarters ago by someone who's not at the company anymore. It's not anyone's fault really. We all need to move fast and our marketing team can only do one thing at a time. The bottle neck is constantly moving, but so are our buyers. B2B email has always been a timing and relevance problem. Our old way of assigning signals to templated emails was too generic and goes stale the next week. A lot of our emails going out referenced the wrong price or a different feature set we no longer supported. Even if we got the signal right and sent the email at the right time, sometimes a generic email at the right moment is worse than no email at all. The high-intent moment is precious. Burn it once with a templated email and the prospect learns to ignore you. I didn't even realize that we "spent" the highest-value moments in our funnel on the lowest-quality messages. So we built AI Emails powered by the full signal chain we've been building for four years. No more templates! Drop the step into any orchestration. Some examples we use: - Inbound chat follow up: a qualified chat engager doesn't book. The AI writes the follow up from the chat log. - Intent Trigger: Bombora flags a research surge about "chatbots." AI outreach references case studies in their industry for optimizing website conversion. - Job change: Head of marketing champion moves to a new role. AI sends a welcome and intro at the new account. - Social engagement: Buyer likes a competitor's social post about website de-anonymization. They get an email about how we have the highest identification rates The agent retrieves the full signal chain on every send: chat transcript (when there is one), every page visited that session, enriched person and company data, CRM stage, and any second or third-party signal already on the account. Then it writes the email. You can see what the agent will spit out before each deploy and iterate on the example scenarios until the email reads the way you'd write. After we started implementing this into our signal orchestrator, our reply rates went from 2% to 6% (3x) Setup: open any orchestration, add the AI Email step, set the filter, write the goal and policy in plain English, test, save. Five minutes. Playbooks for each play dropping in the coming days.

Anthropic just posted another banger guide. This one is on building more efficient agents to handle more tools and efficient token usage. This is a must-read for AI devs! (bookmark it) It helps with three major issues in AI agent tool calling: token costs, latency, and tool composition. How? It combines code executions with MCP, where it turns MCP servers into code APIs rather than direct tool calls. Here is all you need to know: 1. Token Efficiency Problem: Loading all MCP tool definitions upfront and passing intermediate results through the context window creates massive token overhead, sometimes 150,000+ tokens for complex multi-tool workflows. 2. Code-as-API Approach: Instead of direct tool calls, present MCP servers as code APIs (e.g., TypeScript modules) that agents can import and call programmatically, reducing the example workflow from 150k to 2k tokens (98.7% savings). 3. Progressive Tool Discovery: Use filesystem exploration or search_tools functions to load only the tool definitions needed for the current task, rather than loading everything upfront into context. This solves so many context rot and token overload problems. 4. In-Environment Data Processing: Filter, transform, and aggregate data within the code execution environment before passing results to the model. E.g., filter 10,000 spreadsheet rows down to 5 relevant ones. 5. Better Control Flow: Implement loops, conditionals, and error handling with native code constructs rather than chaining individual tool calls through the agent, reducing latency and token consumption. 6. Privacy: Sensitive data can flow through workflows without entering the model's context; only explicitly logged/returned values are visible, with optional automatic PII tokenization. 7. State Persistence: Agents can save intermediate results to files and resume work later, enabling long-running tasks and incremental progress tracking. 8. Reusable Skills: Agents can save working code as reusable functions (with SKILL .MD documentation), building a library of higher-level capabilities over time. This approach is complex and it's not perfect, but it should enhance the efficiency and accuracy of your AI agents across the board. anthropic. com/engineering/code-execution-with-mcp

Your AI agent sent 200 emails this morning. Dashboard says delivered.  Gmail says spam folder. One agent I rebuilt went from 47% inbox rate to 94% on the same list.  Zero copy changes. The problem isn't the copy. Agents don't generate engagement signals. No opens, no clicks, no replies. Just volume. Mailbox providers read that as "cold sender ramping too fast" and start dropping you into promotions or spam before anyone reads a subject line. You won't see it in your agent logs.  Everything looks shipped. Three things I've changed in every agent stack I run now: 𝟭. 𝗥𝗲𝗮𝗱 𝘀𝗲𝗻𝗱𝗲𝗿 𝗵𝗲𝗮𝗹𝘁𝗵 𝗕𝗘𝗙𝗢𝗥𝗘 𝘀𝗲𝗻𝗱𝗶𝗻𝗴 → Warmy shipped API v2 on April 2 → GET /domains and GET /warmup/statistics let the agent check its own reputation on every run → Postmaster data dips, the agent pauses itself 𝟮. 𝗥𝗼𝘂𝘁𝗲 𝗰𝗼𝗺𝗽𝗹𝗮𝗶𝗻𝘁 𝘀𝗶𝗴𝗻𝗮𝗹𝘀 𝗯𝗮𝗰𝗸 𝘁𝗼 𝘁𝗵𝗲 𝗮𝗴𝗲𝗻𝘁 → The new Feedback-ID header maps spam complaints to the exact campaign that caused them → Your agent can quarantine its own misfires instead of tanking the whole domain 𝟯. 𝗧𝗿𝗲𝗮𝘁 𝘄𝗮𝗿𝗺𝘂𝗽 𝗮𝘀 𝗮 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗹𝗼𝗼𝗽 → PUT /warmup/preferences rebalances the Gmail, Outlook, and Zoho mix whenever provider ratios drift → Run it from the same workflow that runs your outreach Infrastructure was the bottleneck.  It usually is.  Act Now: Test Your Inbox Placement Before Your Next Send Tanks Your Domain. https://lnkd.in/gP9t5MfD Full breakdown in the carousel. When's the last time you checked inbox rate vs. delivered rate? #EmailDeliverability 

Stop hauling entire chat histories. Here’s what actually works. Most AI agents get slower, dumber, and more expensive the longer they run, not because of the model, but because of context bloat. They drag along every old turn, tool call, and half-relevant detail. It’s like carrying yesterday’s to-do list into every conversation. The fix is simple: 1️⃣ Keep only the last few turns that matter. 2️⃣ Summarize everything older into one clean block. 3️⃣ Write it back so the agent starts every run fresh, not blind. Result? Lower cost per task. Fewer retries. Cleaner reasoning. No more “forgot what we were doing.” I’ve been testing this across service ops workflows in Sonoma County, intake, scheduling, follow-ups, and its night and day. Agents stay sharp, latency drops, and debugging gets easy again. No fluff, just ROI.

It's more important you feed in the right info than try nail the perfect prompt. The way businesses build with AI is changing. The shift is happening due to reasoning development & agentic capability (AI is able to figure out how to achieve an objective on its own). That means managing what information your AI agent has access to at any given moment is more important than tweaking prompts. You might think that giving AI agents access to everything would make them smarter. The opposite is true. As you add more info, AI performance declines aka "context rot." So here's what you need to do: - Keep instructions clear, no duplication - Don't overload your AI with complex rules - Give your AI just enough direction without micromanaging - Provide a focused toolkit where each function has a clear purpose, so 1 agent for each function rather than trying to get one agent to do everything - Let AI agents retrieve information on-demand For work that spans hours / days, use 2 approaches: 1. Summarizing conversation history to preserve what matters 2. Give agents the ability to take & reference their own notes The most effective AI deployments treat information as a strategic resource, not an unlimited commodity. Getting this right means faster, more reliable results from your AI investments. Image from Anthropic describing the evolution of prompt engineering into context engineering below. p.s. I'm looking to take on no more than 2 clients who want to build this layer into their business as part of a new framework I'm developing - focus is on B2B marketing.