DEV Community: Sakshi Karn

AI Engineering Mistakes Enterprises Commonly Make

Sakshi Karn — Wed, 14 Jan 2026 18:33:06 +0000

Why AI Projects Die in the Lab: 5 Critical Engineering Mistakes Enterprises Make

The statistics are sobering. Depending on which analyst report you read, between 60% and 85% of enterprise AI projects fail to make it into production. They languish in the "Proof of Concept (PoC) Purgatory"—impressive demos that crumble under the weight of real-world traffic, security requirements, or operational costs.

The tragedy is that these failures are rarely due to the science. The models are smart enough. The failures are almost always due to the engineering.

Enterprises often treat AI adoption as a data science problem ("Hire more PhDs!") rather than a systems engineering problem ("Build better pipelines!"). They apply traditional software practices to non-deterministic models, or worse, they apply no engineering rigor at all, treating AI like magic. To cross the chasm from "cool demo" to "business value," leaders must recognize and avoid these five common AI Engineering mistakes.

Mistake 1: The "Notebook to Production" Fallacy

This is the cardinal sin of early-stage AI teams. A Data Scientist builds a model in a Jupyter Notebook. It works perfectly on their laptop. The team then tries to wrap that notebook in a Docker container and ship it to production.

- Why it Fails: Notebooks are non-linear, difficult to test, and impossible to version control effectively. They are scratchpads, not production code. Shipping a notebook leads to "it works on my machine" syndrome at an industrial scale.

- The Engineering Fix: Modularization, AI Engineers must refactor notebook logic into modular, testable Python packages. The training pipeline must be decoupled from the inference code, ensuring that what runs in production is a lightweight, optimized binary, not a sprawling research environment.

Mistake 2: Over-Engineering the Model, Under-Engineering the Data

Teams often obsess over model selection—"Should we fine-tune Llama 3 or use GPT-4?"; while ignoring the quality of the data feeding that model.

- Why it Fails: In the world of RAG (Retrieval-Augmented Generation), a genius model fed garbage data will confidently produce garbage answers. Spending months fine-tuning a model on dirty, duplicate, or outdated documents provides a lower ROI than spending weeks cleaning the data.

- The Engineering Fix: Data-Centric AI, Shift the engineering effort from "Model Ops" to "Data Ops." Build robust ETL pipelines that deduplicate, chunk, and verify data before it ever touches the vector database. The best model is usually just the one with the cleanest context.

Mistake 3: Ignoring Cost Observability (The "Token Burn")

In traditional software, an inefficient loop might slow down the CPU. In Generative AI, an inefficient loop burns cash.

- Why it Fails: Developers used to free/cheap APIs often design "chatty" agents that make 10 calls to an expensive LLM to solve a simple problem. Without cost guardrails, a pilot project can blow through its annual budget in a month once users start engaging.

- The Engineering Fix: Token Economics & Caching. Implement semantic caching (e.g., GPTCache) so that if User A asks a question User B already asked, the answer is served from the cache for free. Engineer "Router" layers that send simple queries to cheaper, faster models (like GPT-3.5 or Haiku) and reserve the expensive models (GPT-4 or Opus) only for complex reasoning.

Mistake 4: The "Generalist" Delusion

Enterprises often try to build "One Bot to Rule Them All", a single agent that has access to HR, Sales, Engineering, and Finance data.

- Why it Fails: As discussed in previous posts, "Context Dilution" kills accuracy. A bot trying to be an expert in everything usually ends up being an expert in nothing, getting confused by overlapping terminology across departments.

- The Engineering Fix: Agent Specialization, Architect a swarm of specialized, narrow agents rather than one monolithic brain. Build a specific "HR Benefits Bot" and a separate "Java Code Assistant." Orchestrate them, don't merge them.

Mistake 5: Skipping Automated Evaluations (The "Vibes" Check)

The most dangerous mistake is deploying a model because "it feels right" during manual testing.

- Why it Fails: AI is probabilistic, A model change that improves answers for Query A might unknowingly degrade answers for Query B. Without automated regression testing, you are flying blind.

- The Engineering Fix: Deterministic Evals, Build a "Golden Dataset" of questions and verified answers. Every time the engineering team modifies the prompt or the retrieval logic, run an automated test suite that scores the new responses against the golden set. Never deploy based on vibes.

Visualizing the Failure Mode: The PoC Trap

The difference between a failed experiment and a successful product is the engineering infrastructure surrounding the model.

How Hexaview Fixes Broken AI Projects

At Hexaview, we often step in when an internal initiative has stalled. We act as the AI Engineering rescue team.

We turn "science projects" into "software products" by:

- Audit & Refactor: We review your notebooks and refactor them into production-grade, containerized microservices.

- Cost Optimization: We implement semantic caching and model routing to slash your inference costs by up to 40%.

- Pipeline Construction: We build the automated evaluation pipelines that give you the confidence to ship updates without breaking the user experience.

We don't just build AI; we engineer the systems that keep AI alive.

Agent-to-Agent Collaboration: Building Multi-Agent Workflows in Salesforce With Agentforce

Sakshi Karn — Mon, 12 Jan 2026 10:11:15 +0000

In today’s fast-paced business environment, managing complex workflows across departments can be challenging. Manual handoffs, fragmented communication, and repetitive tasks often slow operations, reduce efficiency, and increase the risk of errors.

Agentforce, Salesforce’s AI-powered solution, is transforming workflow orchestration by enabling teams of intelligent AI agents to collaborate seamlessly, driving faster, more accurate, and compliant business processes.

How Multi-Agent Workflows Enhance Business Efficiency

Unlike traditional automation, Salesforce multi-agent workflows leverage a team of AI agents, each with specialized roles and capabilities. The primary agent acts as the orchestrator, coordinating tasks among agents responsible for pricing, customer service, compliance, or data verification.

Industry example: In financial services, a primary agent can delegate data validation to one AI, risk assessment to another, and compliance checks to a third. This ensures loan approvals are processed quickly while maintaining regulatory standards. Similarly, in e-commerce, one agent manages product recommendations, another updates inventory, and a third handles customer queries, enabling seamless order fulfillment without delays.

By automating these tasks, human employees can focus on strategic, high-value activities rather than repetitive processes, boosting overall productivity.

Assistive vs. Autonomous Agents

Agentforce supports multiple levels of agent autonomy to meet diverse business needs:

Assistive Agents act as copilots, offering insights and recommendations while humans make the final decision. For example, in a retirement planning firm, an assistive agent may suggest optimal investment portfolios based on client risk profiles, leaving the advisor to finalize the plan.
Autonomous Agents operate independently, executing tasks end-to-end and escalating only when necessary. In insurance claims processing, autonomous agents can route claims, verify documents, and flag exceptions, significantly reducing turnaround time.
Both agent types include built-in guardrails to prevent errors, hallucinations, and security risks, ensuring trust and compliance in regulated industries like banking, healthcare, and insurance.

Key Features and Benefits of Agentforce

Agentforce offers capabilities designed to improve collaboration and workflow efficiency:

- Real-Time Task Routing: Assigns requests to the right agent or team member based on expertise and workload.

- Dynamic Adaptation: Workflows learn from past performance, adjusting to seasonal trends or evolving business needs. For instance, retail operations can automatically optimize staffing during peak holiday seasons.

- Seamless Collaboration: Consolidates communications and audit trails in virtual workspaces, reducing email clutter and ensuring transparency.

- Custom Actions and Topics: Developers can create modular actions under topics such as order management, pricing, or compliance.

- Integration With External Systems: Connects to platforms like AWS or Amazon Bedrock to access external data and execute specialized tasks. A healthcare provider, for example, can safely retrieve patient data from external systems for claims processing while maintaining HIPAA compliance.

Best Practices for Implementing Multi-Agent Workflows

Map existing workflows to identify bottlenecks.
Start with a single department before scaling enterprise-wide.
Monitor KPIs, collect feedback, and optimize iteratively.
Use Agentforce testing tools to simulate real scenarios and ensure reliability.

Driving Smarter Operations With Salesforce Automation

By combining AI intelligence with human oversight, Agentforce enables organizations to streamline complex workflows, improve interdepartmental collaboration, and maintain compliance in real time. Businesses achieve faster turnaround times, minimize errors, and free teams to focus on strategic initiatives.

Agent-to-agent collaboration in Salesforce is more than automation—it’s intelligent workflow orchestration. With applications across financial services, insurance, healthcare, and retail, Agentforce is redefining how enterprises execute multi-step workflows, transforming them into efficient, coordinated, and intelligent operations.

How Hexaview Excels in Multi-Agent Salesforce Workflows

Hexaview Technologies is at the forefront of implementing Agentforce-powered multi-agent workflows for enterprises. By leveraging deep Salesforce expertise, Hexaview helps organizations:

Design and deploy customized multi-agent orchestration tailored to industry-specific needs.
Ensure seamless integration with external systems, enabling real-time data access and advanced automation.
Implement assistive and autonomous agents effectively, maintaining compliance in regulated environments like banking, fintech, and healthcare.
Continuously optimize workflows using data-driven insights, improving efficiency and reducing operational bottlenecks.

Through its proven approach, Hexaview empowers businesses to maximize the potential of Salesforce automation, enabling smarter, faster, and more coordinated workflows that drive measurable business impact.

Multi-Cloud vs Hybrid Cloud Which Strategy Delivers Better ROI

Sakshi Karn — Fri, 19 Dec 2025 09:51:09 +0000

Cloud Crossroads: Multi-Cloud vs. Hybrid Cloud – Decoding the ROI Equation

The journey to the cloud is no longer a simple choice between AWS, Azure, or GCP. As enterprises mature their cloud strategies, the conversation shifts towards more sophisticated models that leverage multiple environments. Two dominant approaches have emerged: Multi-Cloud and Hybrid Cloud. While often discussed together, they represent distinct strategies with different goals, complexities, and, crucially, different implications for Return on Investment (ROI).

Choosing between (or combining) these models isn't just a technical decision; it's a fundamental business strategy decision impacting cost (FinOps), agility, resilience, and compliance. There's no single "better ROI" answer that applies universally. The optimal strategy depends entirely on your specific workloads, regulatory landscape, existing infrastructure investments, and operational capabilities. Understanding the core differences and ROI drivers is essential for making the right strategic choice for your enterprise.

Defining Multi-Cloud: Best-of-Breed Public Clouds

A Multi-Cloud strategy involves utilizing services from two or more public cloud providers (e.g., using AWS for data analytics, Azure for Microsoft workloads, and GCP for AI/ML). The primary drivers are typically:

Avoiding Vendor Lock-in:
Reducing dependency on a single provider, increasing negotiation leverage.
Leveraging Best-of-Breed Services:
Accessing specialized or superior services offered by different clouds (e.g., GCP's BigQuery, Azure's OpenAI services).
Meeting Specific Regional Needs:
Utilizing providers with stronger presence or compliance offerings in certain geographic locations.

Pros of Multi-Cloud:

Flexibility to choose optimal services for each workload.
Potential for cost optimization by leveraging competitive pricing.
Increased resilience by diversifying provider dependencies.

Cons of Multi-Cloud:

Increased Operational Complexity: Managing disparate environments, APIs, security models, and billing systems requires significant expertise and tooling.
Data Integration Challenges: Moving and synchronizing data between different clouds can be complex and costly (egress fees).
Skill Gaps: Requires teams proficient across multiple cloud platforms.
Potential for Higher Costs (if poorly managed): Lack of centralized FinOps across clouds can lead to duplicated efforts and missed volume discounts.

Defining Hybrid Cloud: Public Cloud Meets Private Infrastructure

A Hybrid Cloud strategy involves integrating one or more public clouds with private infrastructure (which could be traditional on-premises data centers or a dedicated private cloud environment). Key drivers include:

Data Sovereignty & Compliance:
Keeping sensitive data within a private environment to meet specific regulatory or residency requirements.
Leveraging Existing Investments:
Utilizing existing on-premises hardware or private cloud infrastructure.
Low-Latency Requirements:
Running applications closer to end-users or specific hardware located on-premises.
Legacy System Integration:
Connecting cloud-native applications with older, monolithic systems that are difficult or costly to migrate.

Pros of Hybrid Cloud:

Greater control over sensitive data and infrastructure.
Ability to meet specific regulatory or performance requirements.
Potential to leverage existing infrastructure investments.
Flexibility to place workloads in the optimal environment (public or private).

Cons of Hybrid Cloud:

High Complexity: Managing the integration, networking, and security between public and private environments is extremely complex. Requires sophisticated orchestration and management tools.
Significant Upfront & Ongoing Costs: Maintaining private infrastructure (hardware, software, personnel) is expensive.
Slower Scalability (Private Side): The private component lacks the on-demand elasticity of the public cloud.
Potential for Operational Silos: Can create friction between teams managing public vs. private environments if not managed cohesively.

The ROI Equation: It's All About Context

Which strategy delivers better ROI? It depends entirely on your specific circumstances. There is no universal winner. The ROI calculation must consider:

Workload Characteristics: Do your applications benefit significantly from specific "best-of-breed" public cloud services (favors Multi-Cloud)? Or do they have strict data residency or low-latency needs better suited for private infrastructure (favors Hybrid)?
Regulatory & Compliance Needs: Are you subject to regulations requiring data to remain within specific geographic boundaries or on private hardware (favors Hybrid)?
Existing Infrastructure: Do you have significant, unamortized investments in on-premises data centers that you need to leverage (favors Hybrid)?
Team Skills & Maturity: Does your team possess the expertise to manage the complexity of multiple public clouds (Multi-Cloud) or the intricate integration of public/private environments (Hybrid)? Do you have mature FinOps practices to manage costs across environments?
Cost Structure: Can you achieve significant savings through competitive pricing and best-of-breed services (Multi-Cloud)? Or does the cost of maintaining private infrastructure outweigh the benefits (leans away from Hybrid unless strictly necessary)?
Innovation Velocity: Does a Multi-Cloud approach allow faster access to cutting-edge services? Does a Hybrid approach slow down DevOps automation due to private infrastructure constraints?

Generally:

Multi-Cloud ROI often comes from flexibility, best-of-breed optimization, and potentially lower TCO if managed well.
Hybrid Cloud ROI often comes from meeting specific compliance/performance needs and leveraging existing assets, but frequently carries higher complexity and operational costs.

Multi-Cloud vs. Hybrid Cloud: Key Differentiators

Understanding the core focus and drivers helps clarify the strategic choice.

How Hexaview Guides Your Cloud Strategy ROI

Choosing between Multi-Cloud and Hybrid Cloud (or designing a strategy that incorporates elements of both) is a critical decision with long-term financial and operational implications. At Hexaview, we provide expert, data-driven cloud strategy consulting as part of our product engineering services.

We don't push a specific model. Instead, we work with you to analyze your workloads, assess your regulatory requirements, evaluate your existing infrastructure, and understand your team's capabilities. Our deep expertise in cloud-native architecture, DevOps automation, and FinOps practices allows us to model the true Total Cost of Ownership (TCO) and potential ROI for each scenario.

Whether the optimal path involves architecting a sophisticated Multi-Cloud environment leveraging best-of-breed services, or designing a secure and efficient Hybrid Cloud integration, Hexaview provides the custom software development and cloud-native product development expertise to build and manage it effectively. We ensure your cloud strategy is not just technically sound, but optimized for maximum business value and return on investment.

How Agentic AI Will Automate 70% of Repetitive Business Operations

Sakshi Karn — Tue, 09 Dec 2025 18:52:50 +0000

*The Autonomous Enterprise: Why Agentic AI Is the Next Leap in Business Automation *

For the past five years, the enterprise automation landscape has been dominated by two technologies: Robotic Process Automation (RPA) and, more recently, AI copilots. RPA gave us "bots" that could mimic human clicks and keystrokes to automate simple, repetitive tasks. AI copilots gave us intelligent assistants that could understand our intent, generate content, and act as a co-creator. These were, and are, transformative.

But both technologies share one fundamental limitation: they require a human operator. An RPA bot must be told exactly what to do. A copilot must be prompted to take its next action. We are now on the verge of the next, far more profound, evolutionary leap: Agentic AI.

This is the shift from "do what I say" (automation) and "help me do this" (copilot) to "achieve this goal" (agent). This is the technology that will finally deliver on the promise of hyper-automation. While experts have long projected that generative AI could automate 60-70% of all work activities, it is agentic AI that provides the mechanism to turn that potential into reality, especially for complex, multi-step business operations.

What is Agentic AI? From Assistant to Autonomous Actor

To build a proper AI strategy, it is critical to understand this distinction.

- A Copilot is a "Passenger":
It sits next to you in the workflow. You are the driver. It can read the map, suggest a faster route, or change the music, but you have to approve every turn.

- An AI Agent is a "Driver":
You give it a high-level destination (a goal) and it handles the rest. It can perceive its environment, make its own decisions, take a series of actions, learn from the results, and dynamically course-correct until the goal is achieved.

An agent is, in essence, an autonomous system designed to execute complex, multi-step workflows. It can plan, remember, and use tools. It doesn't just generate a piece of code; it can be tasked to build, test, deploy, and monitor an entire microservice on its own. This is the true power of intelligent apps.

The Evolution of Automation: From RPA to Autonomous Agents

The progression from simple automation to autonomous agents is a clear evolutionary path. Each step builds on the last, handing over more complexity and autonomy to the machine.

How Agentic AI Will Automate 70% of Repetitive Operations

The 70% figure isn't about replacing 70% of jobs. It's about automating 70% of the repetitive, multi-step processes that currently consume human time. Agentic AI is the first technology capable of handling the entire workflow, not just one piece of it.

Consider these real-world scenarios:

1. Finance: The Autonomous "Book Close"

Goal: "Close the books for Q3."
Agentic Workflow: The AI agent plans the close process. It autonomously connects to the sales CRM, the HR payroll system, and the expense-reporting tool to pull all necessary data. It identifies anomalies (e.g., a missing invoice from a vendor) and proactively emails the vendor to request it. It generates the draft P&L and balance sheets, and finally, it alerts the human CFO that the books are ready for final review and approval.

2. Supply Chain: The Proactive "Inventory Manager"

Goal: "Ensure our mid-west warehouse never runs out of product #123."
Agentic Workflow: The AI agent continuously monitors real-time sales data, supplier inventory levels, and logistics carrier timelines. It predicts a potential stockout in two weeks. It then autonomously evaluates its options, selects the most cost-effective supplier, and places a purchase order, all without human intervention.

3. Software Development: The "Autonomous DevOps" Agent

Goal: "A user has reported a critical bug in the payment module."
Agentic Workflow: The AI agent reads the bug report. It locates the relevant code in the repository. It drafts a potential fix. It runs the test suite against its new code. When the tests pass, it creates a pull request, assigns it to a senior human developer for the final review, and deploys the patch to a staging environment.

The Human's New Role: From Operator to Executive

In this new "autonomous enterprise," the human's role is elevated. We stop being the operators of the tools and start becoming the executives of the process. We are no longer managing the individual tasks; we are managing a team of highly efficient digital agents. Our job becomes:

Goal Setting: Clearly defining the strategic objectives for our AI agents.
Oversight: Reviewing the final, high-level results and providing feedback.
Exception Handling: Managing the complex, novel, or high-stakes edge cases that still require human judgment.

This shift will require a new kind of custom software development—one focused on building the "brains" of these agents, not just the "hands" of a simple bot.

How Hexaview Is Building the Foundation for Agentic AI

At Hexaview, we are not just watching this future unfold; we are building it. We understand that true agentic AI is not a single product you can buy. It is a complex, orchestrated system that must be custom-built and securely integrated into your enterprise.

Our AI engineering services are designed to create the robust foundations these autonomous agents require. This is the next generation of copilot integration solutions. It involves:

Building the "Senses": Architecting the complex data pipelines and APIs that allow an agent to perceive your live business data.
Building the "Hands": Developing the secure integrations that allow an agent to take action in your core systems (like your ERP, CRM, or cloud environment).
Building the "Brain": Implementing the core logic, memory, and planning capabilities of the agent itself.

This is the pinnacle of modern product engineering services, and it is the new frontier of enterprise automation. We are the partner that can help you move beyond simple automation and copilots to build the autonomous, intelligent apps that will define the future of your industry.