Evaluating AI Agents: From Metrics to Real-World Impact (365 Data Science): 2 hours, US Pricing & Syllabus (2026)

Who it is for

AI developers, product managers and researchers are the stated audience for connecting evaluation metrics to practical meaning.

Who should skip it

Learn the stated Python and ML basics first; unfamiliarity with an AI evaluation framework alone does not rule you out.

Official source · 2026-09-09

Facts checked 7 October 2026 against 365 Data Science’s official page ·

Sources ↗

· Next check November 2026

Course and activity access

Individual courses are included in Self-Study; the plan provides access to certification exams and accredited certificates. Free includes course previews.. Official source

What you actually do

You examine quantitative and qualitative approaches for generative systems, classifiers and RAG pipelines. Named metrics include Goal Success Rate, Context Recall, precision, recall and F1-scores. The scope also includes human feedback, bias and hallucination mitigation. This makes the course relevant when your central question is how to judge an agent's behaviour, not simply how to assemble it.

Start with the decision an evaluation is meant to support. You might be trying to compare behaviours, identify a weakness or decide what evidence is still missing. That framing makes the course's metrics easier to evaluate as a learning choice than simply collecting more metric names. It also keeps the distinction between assembling an agent and assessing an agent clear when you compare nearby courses. Identify what would make an evaluation useful for the specific agent task you have in mind. The stored exclusions also distinguish evaluation from building AI models from scratch. If original model construction is your main objective, that is a reason to compare a different course rather than assuming evaluation and model training are interchangeable.

Official source · 2026-10-07

Before you start

Working knowledge of Python (functions, dictionaries, basic libraries like pandas); Basic understanding of machine learning workflows; No prior experience with AI evaluation frameworks needed

Official source · 2026-09-09

Curriculum

Evaluation-course entries retain their recorded timings, including the separately listed examination. The percentage column uses their summed minutes as its denominator; the short advertised content headline is not a fixed completion deadline.

Entry	Subject	Listed time	Share	Role	Observed
1	Welcome & Foundations	8 min	5.4%		2026-09-09
2	Core Evaluation Principles	17 min	11.4%		2026-09-09
3	Quantitative Metrics & Benchmarking	13 min	8.7%		2026-09-09
4	Evaluating LLM-Powered Agents	14 min	9.4%		2026-09-09
5	Mastering RAG System Evaluation	19 min	12.8%		2026-09-09
6	Human-Centric Evaluation Approaches	12 min	8.1%		2026-09-09
7	Ethical Considerations In AI Evaluation	12 min	8.1%		2026-09-09
8	Practical Evaluation Workflows & Future Outlook	10 min	6.7%		2026-09-09
9	Capstone Project & Course Conclusion	4 min	2.7%		2026-09-09
10	Course Exam	40 min	26.8%	Provider-listed exam or combined project/exam section; duration kept separate from advertised content hours.	2026-09-09

Allocation denominator: 149.0 summed minutes; each displayed share = entry minutes / 149.0 × 100, rounded to one decimal.

US price

Purchase information	Recorded terms
Access and billing	As observed in the United States on 2026-09-09, USD 36 buys one month of Self-Study access at the regular monthly price. This is not a fixed completion total; third-party usage may cost extra.
Refund and retakes	Refund and retake terms are not established in these stored policy fields.

Official source · 2026-09-09 | Official source · 2026-09-09

Alternatives

The alternatives sit close to this course's theme, so examine their concrete activities, preparation and award conditions rather than relying on similar titles. Decide whether you want evaluation within a particular kind of agent workflow or a broader introduction to the subject. No numerical score or universal winner is implied by this list.

Building and Evaluating Data Agents

Provider and format	Level	Workload	Award	US price observation
DeepLearning.AI · Online self-paced learning with video lessons and listed activities	Intermediate	1h59m listed course duration	DeepLearning.AI Accomplishment	Currency not specified 0. As shown in the United States on 15 October 2026, the provider support page allows free content access, excluding certificates and Accomplishments. Zero applies only to content access, not a credential-inclusive total. The course header marks its 1 graded assignment as Pro access. No complete third-party API, software or compute budget is established. The course page describes free access as limited to the platform beta; no end date is stated. No numeric Pro subscription price is established by this course page or the visible membership capture.

Entry and assessment questions	Recorded answer
Who is it aimed at?	AI builders extracting, analyzing and producing insights from multiple data sources.
What preparation is required?	Basic knowledge of Python and object-oriented programming is recommended. The course header marks its 1 graded assignment as Pro access. Free content access does not establish access to every listed assessment or project.
How is the award earned?	The outline lists 1 graded assignment. A separate comprehensive final examination and passing threshold are not stated.
What remains unconfirmed?	As checked on 15 October 2026, the captured programme page does not establish academic credit, a separate final exam, expiry or renewal.

Official source · 2026-10-15

Official source · 2026-10-15 | Official source · 2026-10-15 | Official source · 2026-10-15