Virtual Assistant Deployment

𝗗𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝗮𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁? 𝗗𝗼𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗹𝗮𝘂𝗻𝗰𝗵—𝙨𝙩𝙧𝙖𝙩𝙚𝙜𝙞𝙯𝙚. Too often, teams rush to roll out AI agents without a solid deployment playbook. The result? Confused users, poor performance, and broken context threads. Here are 𝗳𝗶𝘃𝗲 𝗯𝗮𝘁𝘁𝗹𝗲-𝘁𝗲𝘀𝘁𝗲𝗱 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 I swear by for deploying AI agents that 𝘥𝘦𝘭𝘪𝘷𝘦𝘳 value (𝘐𝘯𝘧𝘰𝘨𝘳𝘢𝘱𝘩𝘪𝘤 𝘣𝘦𝘭𝘰𝘸 𝘧𝘰𝘳 𝘵𝘩𝘦 𝘷𝘪𝘴𝘶𝘢𝘭 𝘭𝘦𝘢𝘳𝘯𝘦𝘳𝘴!) ↳ 𝟭. 𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗼𝗳 𝗖𝗹𝗮𝗿𝗶𝘁𝘆 → Define the AI agent’s purpose, tasks, boundaries, and goals.  DO: Be specific. DON’T: Deploy vague, general-purpose agents without direction. ↳ 𝟮. 𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗼𝗳 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → Can your agent handle traffic spikes or real-world stress? DO: Run load tests, evaluate metrics, and scale infrastructure. DON’T: Assume your MVP setup will survive in production. ↳ 𝟯. 𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗼𝗳 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗔𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀 → Context is king—especially in AI conversations. DO: Use memory + Retrieval-Augmented Generation (RAG). DON’T: Let agents lose track of user interactions or history. ↳ 𝟰. 𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗼𝗳 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 → Deployment isn’t the finish line—it’s the start of learning. DO: Monitor in real-time, collect feedback, evaluate data. DON’T: Rely on pre-launch assumptions or ignore post-launch signals. ↳ 𝟱. 𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗼𝗳 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 → Your AI agent should evolve. DO: Continuously refine based on real-world usage. DON’T: Treat deployment as “done.” Whether you're building internal copilots or customer-facing agents, following these principles ensures your deployment is not just functional — but 𝘪𝘮𝘱𝘢𝘤𝘵𝘧𝘶𝘭. Which principle resonates most with your AI roadmap?

I am deploying my own LLM Mistral-7B-instruct with supercharged inference As I work on building a chat assistant with Mistral-7B to help customers navigate complex SAAS platform, I run into an important consideration, how will I scale and serve the LLM running the assistant. Let's look at a scenario: Using one GPU-A100 for deployment, our LLM Mistral-7B can generate 17 tokens per second. Now, lets say, if we have 1000 customers using our assistant at the same time, and average length of response from assistant is 150 tokens, putting the numbers together, our assistant will take 2 hours to process requests at anytime. An average reader's speed is 240 words per minute which we should match so our readers don't get bored but with the above setup, more than half the customers could even be waiting 1 hour to get any text at all. Not good at all for User Experience!! First, lets define the metrics we will use to assess the performance of LLM in the context of deployment: - Latency : Total time taken to process one user query. Important for better UX - Throughput: The number of tokens generated per second by the system. Important for scalability We are going to use a popular framework vLLM for optimization and benchmarking but lets look at the basic principles that vLLM leverages: 1. KV caching: - Transformer decoder architecture generates tokens sequentially and to generate a token, it uses all the past generated tokens. For each new token, a key-value vectors are generated which measures the relevance of the token to previous tokens. - So lets say, if we want to predict xth token, we will need KV vectors for 1...(x-1)th tokens, these vectors can be cached instead of regenerating them for every token, leading to time optimization with a memory trade-off. 2. Continuous batching our main optimization: - We parallelly process batches of customer queries, enhancing throughput. - However, differing response sizes in generative text lead to inefficient GPU memory use. - For examples: lets create a batch of two queries: - 'Delhi is the capital of which country?' -'Tell me about Harry potter' The first requires a brief response, while the second could be lengthy. With equal memory allocation per query, the GPU waits for the longer response to complete, leading to underutilized memory for the shorter query. This results in a hold-up of memory resources that could have been used for processing other queries. vLLM allows the efficient use of GPU memory to cache KV vectors, such that when a query in a batch is finished, another query can start processing in that batch. Observations on using vLLM on a batch of 60 queries: 1. Latency decreased more than 15x with vLLM 2. Throughput increased from 18 tokens/s to 385 tk/s 3. Throughput shows significant boost on large batches Link to reproduce results on colab: https://lnkd.in/ew_S_2WD If you are working on a similar project, you are welcome to share your experience :)

Just dropped a new end-to-end agent deployment tutorial (with a complete guide on GitHub) using Amazon Bedrock AgentCore Runtime ✨ ☁️ 🤖 This is a practical walkthrough of what it actually takes to deploy an AI agent that connects to private data sources like Amazon RDS, step by step. This video will be useful to you if you’re: -Learning how AI agents work -Trying to deploy agents to production -Keeping up with the latest AWS releases in the world of AI -Studying for the AWS AI Practitioner or AWS Generative AI Developer – Professional exam By the end, you’ll be able to: -Convert an existing agent to run on AgentCore Runtime with just a few lines of code -Deploy it with a serverless hosting setup -Connect the agent to a real RDS database inside a private VPC without opening security holes -Deploy everything with AWS CDK (no clickops 😝) -Understand how sessions, versioning, updates, and observability work once the agent is live Watch the video on YouTube and leave a like or comment. This is my first video on the AWS Developers channel ✨ 🔗 Full code and step-by-step deployment instructions are on GitHub so you can follow along in your own AWS account: https://lnkd.in/e3iUg9HW 🎥 https://lnkd.in/eYDRvbuF Amazon Web Services (AWS) #ai #aiagents #agents #aws #cloud #tech

Every enterprise wants to "adopt AI agents." Almost none of them can answer a simple question: where should the first one go? This is what the decision process looks like today: ↳ Someone reads about AI agents at Knowledge or on LinkedIn ↳ Leadership asks the platform team to "start automating with AI" ↳ The team picks a use case based on gut feeling or whoever talks loudest ↳ They build an agent for something that handles 50 tickets a month ↳ Meanwhile, password resets are burning 1,250 incidents with 4.1-hour resolution times ↳ Six months later, the AI initiative has a pilot nobody uses and no ROI story to tell The problem was never the technology. It was the targeting. The Australia release introduces AI Agent Advisor. Here's how it actually works: ↳ It scans your operational data — incidents, requests, interactions — and surfaces 28 automation opportunities ranked by volume, resolution time, and priority ↳ Each opportunity gets matched against existing out-of-the-box agents with a confidence score (92% workflow match, 87% agent match in the demo) ↳ Where no agent exists, it generates resolution steps and lets you create one directly in AI Agent Studio ↳ You see the full agent hierarchy before deploying: 18 workflows, 89 agents, 49 ready to deploy, 21 cloned, 37 created ↳ Everything governed through Now Assist Center and AI Control Tower The approach I'd take with this: start with the top 3 opportunities by volume, deploy the ones with 90%+ confidence scores first, measure for 30 days, then expand. Data-driven, not hype-driven. What makes this different from a typical "AI readiness assessment" is that it doesn't give you a PDF with recommendations. It gives you a deployment pipeline with match scores and one-click activation. The automation strategy stops being a conversation and becomes a backlog with numbers attached to every decision. What's your current method for deciding which process gets an AI agent first — data, gut feeling, or whoever complains the loudest? #ServiceNow #AustraliaRelease #NowAssist

In 23 days, we took a customer from fully manual support to an AI-first workflow where AI now autonomously resolves 75%+ of tickets. The main lesson: it's not about the technology. Models, search, retrieval, tools - all table stakes. What actually mattered: 1. Embedding the system into the existing workflow. We integrated the AI assistant directly into Jira and Confluence while keeping the user experience unchanged. People still reach support the same way, the first response just comes from AI now instead of a human. 2. Data access and permissions.  We organized support knowledge, structured historical ticket data, stripped noisy artifacts, and masked sensitive information. We also defined clear boundaries around what the AI could see in Jira and system logs, and what actions it was allowed to take. 3. Giving the support team real control.  A big part of the work was making the system intuitive for the people running support day to day. Our software is built around the language of the business: workflows, responsibilities, and instructions. That makes it familiar to the support team and lets them keep improving the agents over time without involving developers. 4. Leading the deployment with the customer.  We designed the system architecture, handled the deployment, tested it together with the team, trained people, and rolled it out in stages: partners, power users, then everyone. We managed the process closely, aligned everyone around the goal, and kept the deployment moving at speed. Without that, the timeline could easily have been 5x longer. There was plenty of skepticism at first, which is understandable if your prior experience with AI support comes from a bank or airline chatbot. Once this worked in production, the question became where else it could be applied. That has already led to two more deployments: an onboarding assistant and an internal copilot for navigating customer data. Will share more about those soon.

Shipping a model is easy. Keeping it alive in production is the hard part. AI deployment isn’t one pattern you “pick once.” It’s a toolbox you apply based on risk, latency, scale, and business impact. Here’s how real teams deploy AI when reliability actually matters. - Real-time inference Used when users expect instant responses. Optimized for low latency, validation, caching, and safe fallbacks. - Batch inference Runs on schedules for large datasets. Ideal for forecasting, scoring, and analytics where speed matters less than scale. - Streaming inference Handles continuous event-driven data. Powering anomaly detection, monitoring, and live signals. - Human-in-the-loop Keeps humans in control for high-risk decisions. Common in finance, healthcare, and compliance-heavy workflows. - A/B model testing Compares models on real traffic. Decisions are driven by outcomes, not offline benchmarks. - Shadow deployment Tests new models silently in production. You learn before users feel anything. - Canary releases Rolls out models gradually. Problems surface early, rollbacks stay cheap. - Multi-model routing Chooses models dynamically based on cost, complexity, or intent. Quality and spend stay balanced. - Rollback & failover Assumes failure will happen. Systems recover automatically instead of collapsing. - Containerized services Best for standardized, scalable enterprise deployments with strong operational control. - Serverless deployment Handles bursty workloads efficiently. You pay for execution, not idle time. - Edge deployment Runs models close to users or devices. Critical for low-latency or disconnected environments. - Blue–green deployment Switches traffic instantly between environments. Zero downtime during major changes. Production AI isn’t about picking the “best” model. It’s about choosing the right deployment pattern for the risk you’re taking. If your AI works in demos but feels fragile in production, the problem isn’t the model, it’s the deployment strategy. Follow Vaibhav Aggarwal For More Such AI Insights!!

The WEF and Capgemini released a framework in November that tackles how to deploy AI agents that act independently without creating liability exposure you can't defend. When autonomous agents execute transactions without human approval, your organization owns the outcome directly. The framework provides three deployment gates. First is containment during pilots. Test new agent capabilities in environments where failure is recoverable. Understand how the system actually behaves before expanding its authority. Second is evidence-based iteration. Moving an agent from pilot to production depends on documented performance against pre-defined metrics. Third is proportionate safeguards. An agent handling public data operates under different constraints than one managing customer financial information. For product managers, every time you grant an agent new authority—access to different data sources, ability to execute transactions, integration with external systems—you need a defensible record connecting the capability, the risk analysis, and the safeguards implemented. For counsel, the framework provides three control mechanisms: mandatory sandboxing policy with minimum duration requirements, go/no-go decision gates requiring legal approval based on performance data, and risk-based policy tiers rather than categorical restrictions. Why this matters: when an agentic system creates harm—unauthorized transactions, data exposure, discriminatory outcomes—you need to demonstrate you deployed deliberately. "Deliberately" means you documented the risks, implemented proportionate controls, and gathered evidence at each expansion stage that justified increasing autonomy. Agent failures create different liability patterns. The organization that deployed an autonomous agent owns the outcome more directly than with recommendation systems. The framework's staged approach creates decision points where you explicitly chose to expand agent authority based on documented evidence—the record you need when defending that choice. https://lnkd.in/eD4DvSmx For the full story, and to learn more about AI, Innovation and the law, click on the website in my bio.

2025 felt like the year of virtual assistants in home care. And now in 2026, more often than not, I talk to teams with virtual (mostly offshore) team members in roles across their office functions. For Care Advantage, Inc. adding virtual assistants started as a cost strategy and has now quickly become a growth strategy. EVP, Olivia Jones shares how her initial skepticism gave way to a fully integrated model supporting both recruiting and on-call operations. Today, a team of 6 VAs drives the top of the recruiting funnel—each completing ~220 calls per week and collectively bringing in 40+ new caregivers weekly, while also placing dozens into their new caregiver training program. Olivia explains how they hire, onboard, and train VAs like any other team member, and why that’s critical to success. (THIS is where I see most agencies go wrong.) She also unpacks how they’re unraveling a 30+ person, reactive on-call model with a proactive, live-answer approach that improves both cost and quality. (This is my favorite part—I find it FASCINATING.) The integration of VAs is creating a new hybrid operating model that’s helping Care Advantage cut costs, improve quality, and increase accessibility. Get the full story on this week's episode of Home Care Strategy Lab.

No more manual hand-offs — automatically deploy code updates with agentic AI. We all know that AI Agents are the heart of agentic systems/applications. Agentic AI operates via a four-step loop—perceive, reason, act, and learn—allowing it to continuously adapt and improve without constant human prompting. There are some essential components needed to build an AI agent, including a powerful large language model (LLM) for reasoning, a memory layer, access to tools via APIs, and an orchestration framework like LangChain, AG2, or CrewAI to manage the workflow. Let's consider a detailed "real world example" centered around deploying a code update using a code deployment agent. When deploying a code update, a traditional AI assistant might only help you write the necessary deployment script. In contrast, the code deployment agent goes "way further" by taking continuous, autonomous actions to achieve the defined goal. 1. Detection and Preparation: It can detect the new code push. 2. Execution: It pulls the repository (repo), runs tests, and checks for breaking changes. 3. Deployment: It chooses the correct deployment pipeline and pushes the update live. 4. Communication: It notifies the team on Slack. 5. Autonomy: Crucially, it manages these steps without needing human input "every step of the way". The user could give the agent a simple command, such as "ship version 1.2 to staging," and the agent would handle everything, including pulling the repo, checking configurations, kicking off the deployment, and logging the results. Handling Failures and Learning A core capability of this agentic system is its ability to adapt and self-correct. If the deployment breaks, the agent can take necessary remediation steps depending on how it is configured: • It can roll back the changes. • It can look into logs. • It can raise a ticket Know more about agetic AI: https://lnkd.in/gMMp2UDX Learn how to build agentic AI systems in 10 mins: https://lnkd.in/gdFDkjJX