Agentcore Camp trains engineers and teams to design, evaluate, and deploy LLM systems and autonomous AI agents, taught by practitioners who have shipped them at scale. We teach your role, your stack, and the problems your industry actually has.
Most AI courses teach the API. We teach the engineering: the architecture choices, failure modes, evaluation discipline, and cost trade-offs that decide whether an AI system survives contact with real users.
Separate tracks for ML engineers, backend/platform engineers, data scientists, and technical leads. Each track has its own depth, labs, and capstone, so nobody sits through content they don't need.
Labs use realistic data and constraints from finance, healthcare, e-commerce, manufacturing, and enterprise SaaS, including compliance, latency budgets, and messy, unstructured data.
Every program ends with a deployed, evaluated, monitored system rather than a notebook. You leave with code, an eval harness, and a deployment playbook you can reuse.
Instructors are working engineers and researchers who have built ML and LLM systems in industry, not full-time lecturers. You learn the lessons from real incidents, not slide decks.
We treat evals the way good teams treat tests: golden sets, LLM-as-judge calibration, regression gates, and online metrics. That is what separates a demo from a product.
The field moves monthly. Our modules are reviewed every cohort against the latest models, frameworks, and research, so you're trained on what's current, not what was current last year.
Available as public cohorts or as private, customized engagements for engineering teams.
Go from calling an API to owning the full LLM stack: retrieval, fine-tuning, inference optimization, evaluation, and deployment under real latency and cost constraints.
Build autonomous agents that reason, plan, use tools, and recover from failure, and learn when not to use an agent. Focused on reliability, which is where most agent projects fail.
The rigorous foundation every AI engineer needs: the math, the modeling, and the engineering habits that make results reproducible and trustworthy.
We audit your team's current skills and your actual AI roadmap, then build a program around your codebase, data, and use cases. Labs can run on your own infrastructure. Options include on-site or remote delivery, follow-up architecture reviews, and post-training office hours.
A repeatable pipeline that turns learning into skills your team can use the following week.
A technical assessment maps each learner's starting point and goals.
Modules, depth, and labs are matched to role, stack, and industry.
About 70% build time. Real data, real constraints, real failure modes.
Instructors review your code and architecture the way a senior engineer would.
Capstones are deployed, evaluated, and benchmarked against defined targets.
Our instructors have built AI systems inside these industries. Labs reflect their real data shapes, regulations, and success metrics.
Document intelligence, risk and fraud signals, research copilots, and auditable agent workflows under compliance.
Clinical text understanding, literature retrieval, and privacy-preserving pipelines with strict evaluation.
Search and recommendation, conversational shopping agents, and content generation at scale.
Internal knowledge agents, support automation, IT/ops agents, and predictive analytics on sensor data.
We track what happens after training, not just attendance.
[ Problem → what the team built after training → measurable result, e.g. latency, accuracy, cost, or time saved. ]
[ Problem → what the team built after training → measurable result. ]
[ Problem → what the team built after training → measurable result. ]
For the LLM and Agent tracks, you should be comfortable with Python and have basic ML or backend experience. The ML Foundations track starts from programming fundamentals. Our skill diagnostic places you on the right path.
Mostly hands-on. Around 70% of time is spent building, with theory introduced only where it changes an engineering decision.
Yes. Private engagements are tailored to your stack, data, use cases, and compliance needs, and can run on your own infrastructure.
We are vendor-neutral. You'll work with both commercial APIs and open-weight models, and learn how to choose between them on cost, quality, latency, and data control.
A deployed capstone system, its evaluation suite, reusable templates and playbooks, and a reviewed portfolio project.
Tell us about your team, your goals, and where you're stuck. We'll reply with a recommended track or a custom training proposal.