Challenges In AI Real-Time Data Handling

𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗜𝘀 𝗮 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Much of today’s conversation around AI agents focuses on #graphs, #models, #prompts, #context, or orchestration #frameworks. These topics matter, but they rarely determine whether an AI system succeeds once it moves from prototype to enterprise production. The real challenges appear when AI systems operate inside long-running business workflows. Consider a workflow that analyzes documents, retrieves data from multiple systems, calls APIs, and produces a structured decision. Such processes may run for twenty or thirty minutes and involve dozens of steps. Now imagine something routine happens: a network call fails, an API times out, or a container restarts. No problem, the agent says. It starts the workflow again. That may be acceptable for chatbots. It quickly becomes impractical for enterprise processes such as financial analysis, document processing, underwriting, or claims review. These workflows are long-running, resource-intensive, and deeply connected to operational systems. In these situations, the limitation is rarely the model’s intelligence. More often, the challenge lies in the #engineering #discipline around the system. At Cognida.ai, our focus is on building practical enterprise AI systems rather than demos or PoCs. We consistently find that several principles from #distributedsystems engineering become essential once AI moves into production. Here are three such constructs: 𝗗𝘂𝗿𝗮𝗯𝗹𝗲 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 Agent workflows should not be treated as temporary requests. Each step should persist its state so that if a failure occurs, the system can resume from the last successful step rather than restarting the entire process. In practice, this means workflow orchestration with checkpointed state, deterministic execution, and event-driven recovery. For long-running processes, this is often the difference between a prototype and a production system. 𝗜𝗱𝗲𝗺𝗽𝗼𝘁𝗲𝗻𝘁 𝗔𝗰𝘁𝗶𝗼𝗻𝘀 AI agents increasingly trigger real-world actions: sending emails, calling APIs, updating records, moving files, or initiating financial transactions. Retries are inevitable in distributed systems. If actions are not idempotent, retries can create duplicate or inconsistent results. Reliable AI systems must ensure the same action cannot run twice unintentionally. 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗦𝘁𝗮𝘁𝗲 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗠𝗼𝗱𝗲𝗹 Large language models operate within limited context windows rather than durable memory. Enterprise workflows often run longer and across many stages. The system managing the workflow must maintain its own persistent state instead of relying on the model’s temporary context. It means treating AI workflows as structured state machines, not simple prompt-response interactions. Are you treating AI workflows more like state machines, event-driven systems, or traditional #microservices? #PracticalAI #EnterpriseAI

Why Data Architecture is Key to Scalable AI Solutions? Top 10 Reasons Based on On-Ground Practical Experience   1. Eliminates Data Silos Impact: Siloed data systems (e.g., CRM, ERP, IoT) create fragmented inputs, leading to biased or incomplete AI models.
 Example: A retail chain’s inventory AI failed because POS data was isolated from supply chain systems, causing stockouts.
 Practical Fix: Unified data lakes/warehouses (e.g., Snowflake, Databricks) centralize data for cross-functional AI use.   2. Ensures Data Quality at Scale Impact: Poor-quality data (missing values, duplicates) reduces AI accuracy by 30–50%.
 Example: A bank’s fraud detection model generated false positives due to unclean transaction records.
 Practical Fix: Automated data validation pipelines (e.g., Great Expectations, Trifacta) enforce quality before AI ingestion.   3. Enables Real-Time Data Processing Impact: Batch-processed data delays AI insights, rendering them irrelevant for dynamic decisions.
 Example: A ride-hailing company’s surge pricing AI lagged due to hourly data updates.
 Practical Fix: Streaming platforms (e.g., Apache Kafka, AWS Kinesis) feed real-time data to AI models.   4. Supports Massive Compute Workloads Impact: Legacy systems crash under AI’s computational demands (e.g., deep learning, NLP).
 Example: A manufacturer’s predictive maintenance model overloaded on-prem SQL servers.
 Practical Fix: Cloud-native architectures (e.g., Azure Synapse, Google BigQuery ML) scale elastically for AI workloads.   5. Reduces Preprocessing Overhead Impact: 40–60% of AI project time is wasted cleaning and reformatting data.
 Example: A healthcare AI team spent 3 weeks aligning EHR, lab, and imaging formats.
 Practical Fix: Standardized schemas and metadata tagging cut preprocessing time by 50%.   6. Mitigates Compliance Risks Impact: Non-compliant data usage (e.g., GDPR, HIPAA) leads to fines and reputational damage.
 Example: A fintech firm faced €2M GDPR fines after AI processed non-consented user data.
 Practical Fix: Built-in governance tools (e.g., Collibra, Alation) automate compliance checks.   7. Accelerates Model Training & Deployment Impact: Slow training cycles (weeks/months) delay ROI and market responsiveness.
 Example: An e-commerce firm took 6 months to deploy a recommendation engine.
 Practical Fix: MLOps pipelines (e.g., MLflow, Kubeflow) automate model training and deployment.   Continue in first 2 comments. (Bottom Line: Data architecture isn’t an IT problem—it’s a business enabler. Leaders who deprioritize it risk stranded AI investments and irrelevance.)   Image Source: McKinsey   Transform Partner – Your Strategic Champion for Digital Transformation

Enterprise teams are all too aware of the complexity of the data journey through their organizations. There’s a twofold challenge here. Consider the operational reality these organizations face: Enterprise data flows through sophisticated architectures: → Multiple ingestion points and data sources → Complex processing and transformation layers → Distributed storage across various global systems → AI training pipelines and real-time inference systems The twofold challenge is this: First, maintaining all critical data context throughout every stage of these data flows. Second, doing so systematically and without human-in-the-loop requirements that get in the way of scalability. The system that helps enterprises overcome this twofold challenge MUST include: • Tracking of data provenance and lineage • Inheritance of permissions across transformations • Enforcement of consent in real-time systems • Cross-jurisdictional compliance requirements When this context is lost or inconsistent, AI initiatives face an impossible choice: proceed with unknown risk, or halt for manual verification that just cannot scale? This is the challenge our Fides suite addresses for enterprise clients. → Helios provides systematic data discovery and context preservation → Janus manages consent and permissions at scale → Lethe orchestrates data operations across distributed systems → Astralis enforces policies through automated infrastructure, including the scaffolding for AI innovation The AI transformation is accelerating. The winners will be those who solve data context and governance not as a process problem, but as an engineering problem. How is your organization maintaining data context throughout complex AI workflows currently?

🤯 The "Why" - Building the Data-First AI Agent Why do so many AI agents fail? It's not the model, the prompts, or the framework. It's the data. You're seeing the painful symptoms: Agents hallucinating incorrect answers. Agents failing to complete simple tasks. Agents giving generic, unhelpful responses. If this sounds familiar, the problem isn't your AI—it's that you've built it on a data swamp. I've spent years writing about clean data and robust databases because "garbage in, garbage out" has never been more critical. A successful AI agent isn't built with better prompts; it's built on a better data foundation. Here’s how a data-first approach solves the biggest AI agent failures: 📌 Problem: The Agent Hallucinates and Gives Wrong Answers. Your agent confidently tells a customer your return policy is 90 days… but you changed it to 30 days last quarter. This breaks trust instantly. Data-First Solution: The agent uses Retrieval-Augmented Generation (RAG) connected to a clean, version-controlled, and continuously updated knowledge base. Your data pipeline becomes the source of truth, ensuring the agent provides accurate information every time. 📌 Problem: The Agent Can't Take Action. A customer asks, "Where is my order?" and the agent can only reply, "A human agent will get back to you with details." This defeats the purpose of automation. Data-First Solution: The agent has secure, real-time API access to your core business systems (Shopify, Salesforce, etc.). It doesn’t just talk about the order; it retrieves the tracking status directly from the source, providing instant, actionable answers. 📌 Problem: The Agent Lacks Personalization and Context. Every customer gets the same generic greeting and troubleshooting steps, regardless of their history, leading to frustration and churn. Data-First Solution: The agent is integrated with your CRM or Customer Data Platform (CDP). It knows the customer's purchase history, past support tickets, and even their status (e.g., VIP). The conversation starts with rich context, making the customer feel understood from the first message. Stop blaming the LLM. The most powerful and reliable AI agents are built data-first. Before you write another prompt, audit your data pipeline. That's the real foundation. Save 💾 ➞ React 👍 ➞ Share ♻️ #DataFirst #AIAgents #LLM #DataQuality #RAG #AIStrategy #CX

AI breaks because of data. You can have the best architecture, the latest LLM, and powerful infrastructure… but poor data will quietly destroy everything underneath. Here are the hidden data problems that derail AI systems 👇 1. Missing Context Lack of surrounding information leads to incomplete understanding, causing models to generate irrelevant or low-quality outputs. 2. Stale Data Outdated datasets produce incorrect insights, making real-time decisions unreliable and often misleading. 3. Data Silos Disconnected systems prevent a unified data view, limiting model learning and reducing overall performance. 4. Schema Drift Changing data structures break pipelines and introduce unexpected failures in production environments. 5. Duplicate Records Repeated entries confuse models, reducing accuracy and creating inconsistent predictions. 6. Incomplete Data Missing fields weaken model reliability and significantly impact prediction quality. 7. No Data Ownership Unclear accountability leads to inconsistent data quality, lack of governance, and operational confusion. 8. Poor Data Quality Noisy or incorrect data directly impacts model accuracy and weakens decision-making capabilities. 9. Unstructured Chaos Unorganized text data without labeling makes retrieval, reasoning, and processing extremely difficult. 10. Lack of Metadata Without proper tagging, data becomes hard to search, filter, and interpret correctly. [Explore more in the post] What This Means AI systems are only as strong as the data they are built on. Ignoring data problems leads to fragile, unreliable systems. Fix your data pipeline before optimizing your models. Strong data foundations are what make AI actually work. Which of these data issues have you faced the most in your AI projects? Follow Vaibhav Aggarwal For More Such Insights!!

𝗧𝗟;𝗗𝗥: If you thought your data systems aren't ready for GenAI then they definitely are not ready for AI agents! A new University of California, Berkeley study shows a critical insight: LLM agents will soon dominate data workloads, and they behave fundamentally differently than humans, APIs and will cripple most data systems. Paper: https://lnkd.in/eyYcS3DY 𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗗𝗮𝘁𝗮 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Unlike human queries, agents employ "agentic speculation" — issuing hundreds of exploratory queries per second to build understanding. Their research shows agents can increase success rates by 14-70% through this approach, but current databases choke on the volume. This is what we are also seeing with Identity systems (another post forthcoming on that) 𝗧𝗵𝗲 "𝗔𝗴𝗲𝗻𝘁-𝗙𝗶𝗿𝘀𝘁" 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 The paper proposes redesigning data systems around four key characteristics:  • Scale: Supporting massive parallel speculation  • Heterogeneity: Mixed exploration vs. solution phases  • Redundancy: 80-90% query overlap for optimization  • Steerability: Proactive guidance reduces queries by 20%+ 𝗞𝗲𝘆 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀:  • "Probes" beyond SQL with natural language context  • Agentic memory stores for semantic caching  • Shared transaction managers for branched updates  • 𝘚𝘢𝘵𝘪𝘴𝘧𝘪𝘤𝘪𝘯𝘨 optimization vs. complete answers 𝗔𝗰𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗖𝗗𝗢𝘀:  • Audit current query patterns for agent readiness  • Invest in semantic caching and approximate query processing  • Design for speculation, not just transaction throughput  • Start now — this isn't theoretical; agents are already here The future of data belongs to our AI overlords. Time to redesign accordingly. Awesome work by the Berkley team!: Shu (Lynn) Liu, Soujanya Ponnapalli, Shreya Shankar, Sepanta Zeighami, Alan Zhu, Shubham Agarwal, Ruiqi Chen, Samion Suwito, Shuo Yuan, Ion Stoica, Matei Zaharia, Natacha Crooks, Joseph Gonzalez, and Aditya Parameswaran

42% of companies abandoned most of their AI initiatives in 2025. And that didn’t happen because of model limitations, but because of architecture. Recently, IBM completed its $11 billion acquisition of Confluent to enable real-time data access for AI agents. And what I’ve been seeing across operations is the same pattern: agents being deployed on top of fragmented, outdated data with no shared context. Yet, they’re still expected to make reliable decisions and we already know the outcome. Without continuous access to the real state of the operation, agents cannot execute consistently. They interpret partially, make decisions with incomplete context, and end up relying on human intervention to correct deviations. A customer service agent, for example, accesses an outdated CRM, makes a decision based on an old status, and triggers the wrong action. The issue isn’t the model, but the data that supported the decision. And in this scenario, what scales is complexity. That’s why, before discussing agents, models, or use cases, there is a prior layer that needs to be addressed: the ability to provide reliable, real-time data across the entire flow. If your agents had to make decisions today without human intervention, would your architecture support that with real-time data, or does it still depend on invisible manual reconciliations within the process?

Looking at the latest research from the Universität Innsbruck and CASABLANCA hotelsoftware GmbH on RAG implementation in industry - this study offers critical insights into the real-world deployment challenges we're all facing. Key Technical Findings: The research reveals that most industrial RAG systems are still operating at prototype stages (12/13 companies below TRL 7), primarily focused on domain-specific question answering rather than the six application categories outlined in academic literature. Under the Hood Challenges: Data preprocessing emerges as the biggest technical hurdle. The study identifies four critical challenge categories: - Data Management: Handling unstructured data variety across PDFs, images, and documents requires substantial preprocessing effort. Identity recognition becomes complex when the same abbreviation represents different concepts across documents. - Retrieval Component: Determining optimal chunking strategies proves challenging - chunks must be large enough for context but not so large they overwhelm the generator. Embedding strategy selection significantly impacts retrieval quality. - Generator Issues: Hallucination remains a persistent problem, with LLMs failing to accurately convey retrieved information or introducing erroneous details not present in source documents. - System-wide Concerns: Right scope selection and access management across departments create architectural complexity. Industry vs Research Gap: Surprisingly, evaluation remains predominantly manual rather than automated. While academic research has developed frameworks like RAGAS, industry practitioners rely heavily on human assessment due to the lack of domain-specific test datasets. Requirements Reality Check: Security and data protection rank highest (8.5-8.9/10), while ethical considerations and bias mitigation score surprisingly low (5.6/10) - revealing a focus on immediate technical concerns over longer-term AI governance. The bottom line: successful RAG implementation requires modular architecture, significant data preparation investment, and careful chunking optimization. Each use case demands tailored approaches rather than one-size-fits-all solutions.

𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲: 𝗧𝗵𝗲 𝗛𝗶𝗱𝗱𝗲𝗻 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝗡𝗼 𝗢𝗻𝗲 𝗧𝗮𝗹𝗸𝘀 𝗔𝗯𝗼𝘂𝘁 The narrative around AI agents is shifting. It’s no longer just about “how smart” a model is. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸? Data integration and quality. As models improve, enterprises are learning the hard way that access to the right data at the right time is what makes or breaks an AI agent. 🚀 𝗔𝗣𝗜𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗽𝗼𝘄𝗲𝗿 𝗯𝗿𝗼𝗸𝗲𝗿𝘀. They enable AI agents to act, not just predict. Yet, many enterprises still struggle with fragmented data silos, inconsistent governance, and legacy systems that weren’t designed for agentic workflows. 🔎 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝗶𝘀𝗻’𝘁 𝗔𝗜, it's the data plumbing behind it. 𝗖𝗮𝗻 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝘀𝗲𝗮𝗺𝗹𝗲𝘀𝘀𝗹𝘆 𝗽𝘂𝗹𝗹 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲, 𝗰𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹𝗶𝘇𝗲𝗱 𝗱𝗮𝘁𝗮? Do your APIs expose the right business actions without creating security risks? Is your data infrastructure ready for decision automation at scale? 𝗛𝘆𝗽𝗲 𝗶𝘀 𝗲𝗮𝘀𝘆. 𝗨𝘁𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝗵𝗮𝗿𝗱. The enterprises that win in the AI race won’t just have the best models, they’ll have the cleanest, most connected, and most actionable data. Are we underestimating the role of data engineering in making AI agents truly enterprise-ready? Let's discuss.👇 Enjoyed this post? Like 👍, comment 💭, or re-post ♻️ to share with #AI #DataIntegration #EnterpriseAI #APIs #MachineLearning

One of the more technical - but increasingly important - challenges for AI builders today is managing the tradeoff between throughput and latency. As models grow larger and workloads become more complex - particularly in agentic systems that reason, plan, and act in real time- these two priorities often come into conflict. Much like in traditional software systems, optimizing for one can undermine the other. Reasoning workloads unfold in two phases: prefill and decode. Prefill is when the model ingests context and plans a response - it’s compute-intensive and benefits from parallelism. Decode is when the model generates tokens - it requires low latency and high memory bandwidth. Most infrastructure today is optimized for one of these phases, but rarely both. This is where NVIDIA’s GTC announcements were interesting. Their orchestration layer, Dynamo, dynamically reallocates GPU resources between prefill and decode in real time - essentially turning a hard constraint into a tunable parameter. It's one step toward treating throughput and latency as a system-level problem - something to be actively managed through orchestration and software, rather than accepted as a fixed constraint of the underlying hardware. More on this in my March newsletter: https://lnkd.in/gCCdX2ua