Evaluating an Agentic Platform
Discover how to evaluate agentic AI platforms for secure, reliable production use. Learn to assess governance, security, observability, scalability, deployment, data architecture, and cost while preserving flexibility.
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Upon completion of the Evaluating an Agentic Platform skill and assessment, you will earn a Credly Badge that you are able to share with your network. |
Learning Objectives
Evaluate Agentic Platform Infrastructure
Identify the core building blocks of an agentic AI platform and assess how governance, data architecture, and deployment architecture shape what a platform can safely support.
Apply Security and Observability Standards
Identify the security and observability capabilities needed to constrain agent authority, monitor agent behavior, and answer the three core validation questions required for production readiness.
Evaluate Operational and Economic Sustainability
Evaluate platform scalability, reliability, latency, and unit economics to ensure autonomous agents handle concurrent financial workloads without multiplying operational complexity.
Evaluate Platforms for Scale and Longevity
Assess how platform architecture affects performance, multi-region scalability, data latency, and cost forecasting, and evaluate how decoupled flexibility protects against vendor lock-in.
Who is this Course Good for?
This skill badge is designed for technology decision-makers, enterprise architects, and engineering leaders responsible for moving AI agents from prototype into production. If your team has hit the demo-to-production gap — stalled security reviews, unpredictable token budgets, or architectural sprawl across teams — this skill gives you the vocabulary and evaluation framework to fix it. You'll learn to look past any single agent demo and assess the underlying platform infrastructure that determines whether your AI fleet can scale safely, securely, and cost-effectively.
What to Expect in this Course
The skill begins by establishing the Golden Rule: build agents with frameworks, but operate them with platforms. You'll learn to distinguish an individual agent from a harness and a platform, and use the AI Maturity Scale to see why so many teams stall at siloed, hand-rolled orchestration instead of reaching a governed fleet. From there, you'll break down the six core components of an agentic harness — state and persistence, security and governance, orchestration and tool use, memory, observability, and evals — and map how governance, data architecture, and deployment architecture shape a platform's operational boundaries.
Next, the skill turns to enterprise security and observability. You'll learn to formulate the three core validation questions every production review demands: what was the agent allowed to do, what did it actually do, and did it do it safely? You'll identify the security must-haves that constrain agent authority — scoped identity, granular access control, isolation, and context defenses — alongside the observability must-haves that make agent behavior legible, such as stack-wide execution traces and full audit trails. Finally, you'll see how Symbiotic Governance unifies these two domains into a single compliance loop.
The skill then shifts to operational and economic sustainability. You'll compare unified versus split data architectures to evaluate scalability, and map reliability requirements to a tunable recovery spectrum defined by Recovery Time Objective (RTO) and Recovery Point Objective (RPO). You'll analyze how data locality and physical proximity affect agent execution latency, and evaluate cost frameworks to track unit economics while mitigating the four scaling cost drivers: network egress, high-availability topologies, storage multipliers, and split architectures.
Finally, you'll apply the "18-month hedge" to future-proof your platform choice against a rapidly shifting market. You'll explain the architectural value of decoupling execution choices — model, framework, and runtime — from a single centralized governance layer, and evaluate how MongoDB's Atlas Agent Engine delivers run-anywhere deployment, framework and model flexibility, comprehensive observability, enterprise-grade security, and built-in governance.
Summary of the Course
- Differentiate individual agent frameworks from agentic platforms using the Golden Rule.
- Explain why robust infrastructure, rather than the underlying AI model alone, bridges the demo-to-production gap.
- Describe the six components of an agentic harness (Runtime/Execution Layer, Orchestration/Workflow Control, State and Memory, Data and Retrieval Layer, Lifecycle Management, Observability and Evals, Cost Controls) and how they relate to a platform.
- Analyze how Governance, Data Architecture, and Deployment Architecture shape runtime environments.
- Formulate the three core validation questions required to evaluate production readiness.
- Identify critical security must-haves required to constrain agent authority.
- Examine observability must-haves necessary to make complex agent behavior legible.
- Analyze how the intersection of security and observability creates Symbiotic Governance.
- Assess platform scalability by evaluating multi-region deployment and horizontal scaling patterns.
- Determine architectural factors driving platform reliability, mapping them to a tunable recovery spectrum (RTO/RPO).
- Analyze the impacts of data locality and physical proximity on agent execution latency.
- Evaluate cost frameworks to track unit economics and mitigate the "Four Scaling Cost Drivers."
- Apply future-proofing strategic questions to prevent vendor and cloud lock-in over an extended timeframe.
- Explain the architectural value of decoupling execution choices from a single governance layer.
- Evaluate Atlas Agent Engine's technical capabilities across deployment, flexibility, memory, and cost guardrails.
Daniel Curran | Senior Manager, Curriculum Designer
Daniel Curran is a Senior Manager, Curriculum Designer at MongoDB, where he designs hands-on learning experiences that help developers build practical MongoDB skills through labs, skill badges, and technical courses.
His recent work includes curriculum on memory for AI applications, building apps with code agents, and data resilience, with a focus on making complex topics clear, useful, and immediately applicable for learners.
Parker Faucher | Senior Curriculum Engineer
Parker Faucher is a Senior Curriculum Engineer on the MongoDB University team, where he designs and develops technical learning content for developers and data professionals. Based in Phoenix, Arizona, Parker specializes in building skills-based learning experiences across topics like vector search, AI applications, and MongoDB performance, helping learners go from concept to hands-on practice.
With a strong focus on quality and efficiency, Parker also leads efforts to integrate AI tooling into the curriculum development workflow, streamlining reviews, accelerating iteration cycles, and freeing up more time for the work that matters most.
Sarah Evans | Senior Curriculum Engineer
Sarah Evans is a Senior Curriculum Engineer at MongoDB, where she designs and develops technical learning experiences for MongoDB University. With a background that bridges curriculum design and hands-on technical expertise, she specializes in translating complex database and data modeling concepts into clear, accessible content for developers and technology professionals. Sarah is passionate about practical, engaging technical education and brings a deep interest in AI-driven development to her work helping learners navigate the intersection of modern databases and intelligent application design.
Sequoyha Pelletier | Senior Technologist
Sequoyha Pelletier is a Senior Technologist at MongoDB, bringing over 15 years of experience in technical curriculum development and delivery. Before joining MongoDB, he worked in the Worldwide Support team for DataStax, eventually leading the curriculum team for new hire onboarding.
Sequoyha is extremely passionate about providing quality education for free to those in need and enjoys pushing the boundaries of what is considered "normal" practices with delivering educational content.
Imagine building a sleek, state-of-the-art prototype for an autonomous AI agent. The demo looks flawless, leadership is thrilled, and everyone wants it in production immediately.
But the moment you try to deploy it to handle real business operations or financial transactions, everything falls apart. It crashes on simple network timeouts, burns through API budgets overnight, and leaves zero audit trail for compliance. What felt like a massive breakthrough in development instantly turns into an operational liability.
Hi, I'm Sarah, and I'm a Senior Curriculum Engineer at MongoDB. In this skill for Evaluating an Agentic AI Platform, we are going to bridge that demo-to-production gap.
You'll learn how to shift your focus from building single agent prototypes to evaluating the underlying platform infrastructure required to run a secure, reliable, and cost-effective AI fleet.
We'll start by establishing the Golden Rule: build agents with frameworks, but operate them with platforms.
We'll break down the six core components of an agentic harness and map out the AI Maturity Scale to see why so many engineering teams stall before reaching a fully governed fleet.
From there, we'll tackle Enterprise Security and Observability. You'll learn how to enforce Symbiotic Governance by pairing strict security policies with stack-wide execution traces—giving you the proof needed to answer the three core validation questions every security audit demands.
Then, we'll focus on Operational and Economic Sustainability. We'll compare unified versus split data architectures, evaluate RTO and RPO trade-offs, and show you how to eliminate cross-region latency taxes while keeping your cost per unit of work flat as you scale.
Finally, we'll introduce the "18-month hedge," a strategy of decoupled flexibility that protects your enterprise against vendor lock-in.
When we do that we'll examine how MongoDB's Atlas Agent Engine platform delivers radical execution choice at the edge while keeping your core operations ironclad.
By the end of this skill, you will possess a battle-tested framework to evaluate, select, and deploy an agentic platform that scales safely without painting your architecture into a corner. So, if you're ready to take your agents out of the sandbox and into production, let's dive in.
Once you've completed this content, you'll be ready to apply your new knowledge and earn your Evaluating an Agentic AI Platform with MongoDB skill badge. It's more than a digital badge to share on LinkedIn—it's verifiable proof of your ability to evaluate, secure, and scale enterprise agentic infrastructure.
