Developing AI Agents

Many engineers can build an AI agent. But designing an AI agent that is scalable, reliable, and truly autonomous? That’s a whole different challenge.  AI agents are more than just fancy chatbots—they are the backbone of automated workflows, intelligent decision-making, and next-gen AI systems. However, many projects fail because they overlook critical components of agent design.  So, what separates an experimental AI from a production-ready one?  This Cheat Sheet for Designing AI Agents breaks it down into 10 key pillars:  🔹 AI Failure Recovery & Debugging – Your AI will fail. The question is, can it recover? Implement self-healing mechanisms and stress testing to ensure resilience.  🔹 Scalability & Deployment – What works in a sandbox often breaks at scale. Using containerized workloads and serverless architectures ensures high availability.  🔹 Authentication & Access Control – AI agents need proper security layers. OAuth, MFA, and role-based access aren’t just best practices—they’re essential.  🔹 Data Ingestion & Processing – Real-time AI requires efficient ETL pipelines and vector storage for retrieval—structured and unstructured data must work together.  🔹 Knowledge & Context Management – AI must remember and reason across interactions. RAG (Retrieval-Augmented Generation) and structured knowledge graphs help with long-term memory.  🔹 Model Selection & Reasoning – Picking the right model isn't just about LLM size. Hybrid AI approaches (symbolic + LLM) can dramatically improve reasoning.  🔹 Action Execution & Automation – AI isn't useful if it just predicts—it must act. Multi-agent orchestration and real-world automation (Zapier, LangChain) are key.  🔹 Monitoring & Performance Optimization – AI drift and hallucinations are inevitable. Continuous tracking and retraining keeps your AI reliable.  🔹 Personalization & Adaptive Learning – AI must learn dynamically from user behavior. Reinforcement learning from human feedback (RHLF) improves responses over time.  🔹 Compliance & Ethical AI – AI must be explainable, auditable, and regulation-compliant (GDPR, HIPAA, CCPA). Otherwise, your AI can’t be trusted.  An AI agent isn’t just a model—it’s an ecosystem. Designing it well means balancing performance, reliability, security, and compliance.  The gap between an experimental AI and a production-ready AI is strategy and execution.  Which of these areas do you think is the hardest to get right?

Most AI agents don’t fail because they can’t do something. They fail because they can’t do it reliably. Today, spinning up an agent is easy. With LangGraph, CrewAI, LlamaIndex, or OpenAI’s Agent SDK, you can wire up tools, memory, and orchestration in hours. The real challenge? 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆. → Can the agent consistently complete actions without stalling midway? → Can it pick the right tool for the task, not just a “good enough” one? → Can it avoid hallucinations, PII leaks, and unsafe decisions under pressure? → And most importantly, can you trust it enough to put it in front of users or customers? ✅ What you actually need is an agent you can trust end-to-end. And this is where the industry gap is huge: Gartner predicts 40% of agentic AI projects will be canceled by 2027 due to reliability issues. The teams that win treat reliability as a first-class metric, not an afterthought. They measure it the same way they track accuracy, latency, or throughput. That’s why I think Galileo’s Agent Reliability Platform is worth paying attention to. It introduces purpose-built metrics like: → 𝗧𝗼𝗼𝗹 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 → 𝗧𝗼𝗼𝗹 𝗘𝗿𝗿𝗼𝗿 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 → 𝗔𝗰𝘁𝗶𝗼𝗻 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗺𝗲𝗻𝘁 → 𝗔𝗰𝘁𝗶𝗼𝗻 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 … and more The goal is to diagnose and benchmark reliability across real-world, domain-specific workflows to measure and improve agent performance. All designed to systematically benchmark and improve how agents perform in real-world workflows. 👉 If you’re serious about deploying agents in production, check it out here:  https://lnkd.in/dG23PS4P #GalileoPartner

Many AI agents look impressive in demos, but crash in real-world production. Why? Because scaling agents requires engineering discipline, not just clever prompts. Moving from prototype to production means tackling memory, observability, scalability, and resilience challenges. Let’s explore the design principles that make AI agents production-ready. 🔸Why AI Agents Fail Monolithic designs, missing scalability, and poor observability often break agents under real-world traffic. 🔸Microservices Architecture Break agents into services like inference, planning, memory, and tools for flexibility and fault tolerance. 🔸Containerization & Orchestration Use containers for packaging and Kubernetes for orchestration. Make it a habit from prototype to multi-agent production. 🔸Message Queues & Async Processing Prevent bottlenecks with task queues, event sourcing, and non-blocking communication. 🔸Continuous Delivery (CI/CD) Automate deployments with a three-stage pipeline for faster, safer updates. 🔸Load Balancing for Real Traffic Distribute 50–5,000+ requests/minute with API gateways, application layers, and service mesh. 🔸Scalable Memory Layer Use Redis for short-term context, SQL/NoSQL for structure, and Vector DBs for knowledge. 🔸Observability & Monitoring Log calls, monitor latency, and enable human-in-the-loop reviews for deeper debugging. The real test for AI agents goes beyond a demo to survive production traffic at scale. Have you had this experience? #AIAgent

Agentic AI just got real for SAP SuccessFactors teams—inside Salesforce 👉 Agentforce + SuccessFactors: Employees can handle HR tasks against SuccessFactors from Salesforce as the front door—no swivel-chairing. Think: a single chat-style interface that understands context, pulls the right data, and takes the next step. 👉 Live employee data, where service happens: Sync key SuccessFactors attributes into Salesforce so cases, leave queries, and approvals run on accurate data—fast. 👉 Governed by your source of truth: Keep SuccessFactors as the system of record while Salesforce becomes the experience layer for agents and employees. (This is part of the current Salesforce release train.) What this really means: Agentic workflows can observe events (e.g., profile changes), reason over HR policies, and act—open/route cases, populate forms with SuccessFactors data, and draft responses for HR—without sending people hunting across tools. Your HR ops get speed; employees get straight answers. Practical use cases to launch first: 1. Unified HR help: employees ask once in Salesforce, the agent checks SuccessFactors data and resolves or routes. 2. Leave queries: agent pulls balances and policy context; escalates only when needed. 3. On/offboarding: trigger checklists from SuccessFactors changes, keep status visible in Salesforce. If you run SuccessFactors + Salesforce, here’s your chance to switch on agentic HR. #SAPSuccessFactors #AgenticAI #Salesforce #HRService #HCM #HRTech #CHRO

Today, we’re sharing more about one of the most important parts of building trustworthy and reliable AI agents: testing. Every CX and product leader wants agents that are fast, helpful, and on-brand. But with non-deterministic models, even small changes to prompts or your knowledge base can change how an agent behaves. That’s why we built a complete testing suite directly into Decagon: ➤ Unit tests for consistent, policy-aligned responses ➤ Integration checks to ensure the right data gets pulled, tools get triggered, and the agent behaves as intended ➤ Simulations to make sure agents perform reliably across entire workflows, over and over again It all lives in the same place where you define and edit Agent Operating Procedures (AOPs), so CX teams can ship fast without guessing how changes will impact customers. If you’re deploying agents without seeing how they’ll perform in real-world scenarios, you’re flying blind. Check out the full blog in the comments.

Big News from #TDX25! 🚀 Expose Your Apex REST APIs as Agent Actions in Agentforce! Salesforce has introduced a beta feature that enables developers to expose existing Apex REST APIs as agent actions within Agentforce. This integration allows agents to invoke custom business logic encapsulated in Apex REST APIs, thereby enhancing their capabilities. Steps to Integrate Apex REST APIs as Agent Actions: 1. Understand Your Apex REST API: Ensure clarity on the API's behavior, including request and response formats. Test the API using tools like Postman to validate its functionality. 2. Generate an OpenAPI Document: Use Visual Studio Code or Code Builder with the Agentforce for Developers extension installed. Open your Apex class and execute the command: SFDX: Create OpenAPI Document from This Class (Beta). This action creates two files in the externalServicesRegistrations folder: <ApexClassName>.yaml: The OpenAPI document. <ApexClassName>.externalServiceRegistration-meta.xml: Metadata for deployment. 3. Review and Refine the OpenAPI Document: Manually inspect the generated document to ensure accuracy. Make necessary adjustments to align with your API's specifications. 4. Deploy to the API Catalog: Deploy the OpenAPI document and its metadata to your Salesforce organization's API Catalog. This registration makes the API available for creating agent actions. 5. Create Agent Actions: Navigate to Agent Builder in your Salesforce org. Utilize the registered API to define new agent actions based on the operations specified in your OpenAPI document. #Salesforce #Agentforce #sfdcsaga

This is how Aimpoint Digital built an AI agent system to generate personalised travel itineraries in under 30 seconds, saving hours of planning time. - Aimpoint Digital's system uses a multi-RAG architecture -- it has three parallel RAG systems to gather info quickly. Each system focuses on different aspects such as places, restaurants, and events to give detailed itinerary options. - They utilised Databricks' Vector Search service to help the system scale. The architecture currently supports data for 100s of cities, with an existing DB of ~500 restaurants in Paris, ready to expand. - To stay up-to-date, the system adds Delta tables with Change Data Feed. This updates the vector search indices automatically whenever there's a change in source data, keeping recommendations fresh and accurate. - The AI agent system runs on standalone Databricks Vector Search Endpoints for querying. This setup has provisioned throughput endpoints to serve LLM requests. - Evaluation metrics like precision, recall, and NDCG quantify the quality of data retrieval. The system also uses an LLM-as-judge to check output quality from aspects like professionalism, based on examples. Link to the article: https://lnkd.in/gFGvyTT9 #AI #RAG #GenAI

What if your smartest AI model could explain the right move, but still made the wrong one? A recent paper from Google DeepMind makes a compelling case: if we want LLMs to act as intelligent agents (not just explainers), we need to fundamentally rethink how we train them for decision-making. ➡ The challenge: LLMs underperform in interactive settings like games or real-world tasks that require exploration. The paper identifies three key failure modes: 🔹Greediness: Models exploit early rewards and stop exploring. 🔹Frequency bias: They copy the most common actions, even if they are bad. 🔹The knowing-doing gap: 87% of their rationales are correct, but only 21% of actions are optimal. ➡The proposed solution: Reinforcement Learning Fine-Tuning (RLFT) using the model’s own Chain-of-Thought (CoT) rationales as a basis for reward signals. Instead of fine-tuning on static expert trajectories, the model learns from interacting with environments like bandits and Tic-tac-toe. Key takeaways: 🔹RLFT improves action diversity and reduces regret in bandit environments. 🔹It significantly counters frequency bias and promotes more balanced exploration. 🔹In Tic-tac-toe, RLFT boosts win rates from 15% to 75% against a random agent and holds its own against an MCTS baseline. Link to the paper: https://lnkd.in/daK77kZ8 If you are working on LLM agents or autonomous decision-making systems, this is essential reading. #artificialintelligence #machinelearning #llms #reinforcementlearning #technology

About 12-18 months ago I posted about how AI will be a layer on top of your data stack and core systems. It feels like this trend is picking up and becoming a quick reality as the next evolution on this journey. I recently read about Sweep’s $22.5 million Series B raise (in case you're wondering, no, this isn't a paid ad for them). If you're not familiar with them, they drop an agentic layer straight onto Salesforce and Slack; no extra dashboards and no new logins. The bot watches your deals, tickets, or renewal triggers and opens the right task the moment the signal fires, pings the right channel with context, and follows the loop to “done,” logging every step again in your CRM. That distinction matters for CX leaders because a real bottleneck isn’t “more data,” it’s persuading frontline teams to actually act on signals at the moment they surface. Depending on your culture and how strong of a remit there is around closing the loop, this is a serious problem to tackle. You see, when an AI layer lives within the system of record, every trigger, whether that is a sentiment drop, renewal milestone, or escalation flag, can move straight to resolution without jumping between dashboards or exporting spreadsheets. The workflow stays visible, auditable, and familiar, so adoption happens almost by default. Embedding this level of automation also keeps governance simple. Permissions, field histories, and compliance checks are already defined in the CRM; the agent just follows the same rules. That means leaders don’t have to reconcile shadow tools or duplicate logs when regulators, or your internal Risk & Compliance teams, ask for proof of how a case was handled. Most important, an in-platform agent shifts the role of human reps. Instead of triaging queues, they focus on complex conversations and relationship building while the repetitive orchestration becomes ambient. This means that key metrics like handle time shrink, your data quality improves, and ultimately customer trust grows because follow-ups and close-outs are both faster and more consistent. The one thing you will need to consider is which signals are okay for agentic AI to act on and which will definitely require a human to jump on. Not all signals and loops are created equal, just like not all customers are either. Are you looking at similar solutions? I'd be interested to hear more about it if you are. #customerexperience #agenticai #crm #innovation

“Testing AI” is a misleading term. It sounds like a one-off task, but it must be an ongoing job. Testing AI applications is fundamentally different from traditional software testing, yet this distinction is widely misunderstood. Traditional software testing uses preset test cases with predictable inputs and expected outputs, testers simply verify correct results and mark "Pass." This approach is inadequate for AI applications, especially those involving human interactions like patient care, where inputs are virtually infinite and most scenarios are edge cases (unusual situations at the boundaries of expected behavior). Many assume testing ends once a bot answers sample questions correctly. This becomes dangerous in real healthcare deployments. The development paradigm has shifted, though many haven't recognized it. Traditional development allocated roughly 70% effort to building, 10% to testing, and 20% to refinement. Today, these proportions have reversed. The optimal approach is sprinting to deliver a testable version within 20-30% of the timeline, then beginning intensive testing immediately. The remaining 70-80% goes into continuous testing and refinement. We run adversarial tests regularly, not just to confirm functionality, but to understand when and how systems fail. This isn't just good practice; it's essential for responsible AI deployment. Because in healthcare, users don’t follow scripts. They describe problems in five different ways. They skip menus. They confuse symptoms. Sometimes, staff don’t tag the data properly. Sometimes, content updates conflict with information in the existing knowledge base. So you can’t just test AI once. You have to keep testing it, with live data, under real-world conditions, with all the edge cases and chaos that come with actual usage. That’s why we’ve built testing infrastructure into our product lifecycle. The scary part is that most companies don’t do this. They demo a shiny proof of concept and call it done. That’s a false sense of security, and it will break, once your product gets to the user. This is why companies should partner with experienced teams who have battle-tested their solutions through real-world deployment. We've encountered failures, learned from them, and built those insights into rapid iterative improvement cycles.