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Engineers · $497

Production-Grade Agent Engineering.

The engineering deep dive. Eval harnesses, observability (LangSmith / Langfuse / Arize), security boundaries, cost control, retry semantics, and the patterns that survive 2am incidents.

Advanced3.3 hr hands-on16 lessons2 free lessons7-day money-back

What you will learn

Design and run eval harnesses that catch regressions

Wire observability with LangSmith, Langfuse, or Arize

Security boundaries, sandboxing, prompt injection defense

Cost control, retry semantics, and incident playbooks

Fast answer

A production-grade agent engineering course should teach workflow architecture, data preparation, prompt systems, tool integrations, evaluations, monitoring, approval loops, and deployment handover, because implementation is where agent ideas become operational systems. Teams need more than prompts: they need reliable inputs, test cases, logs, named owners, and a plan for failures. The path starts by designing the architecture, mapping trigger, context gathering, model call, tool actions, storage, review, final output, and escalation paths. You then prepare data sources by connecting documents, CRM records, tickets, forms, spreadsheets, or databases with clear source priority and permission rules. Next you create evaluations with test cases for normal inputs, edge cases, missing data, bad instructions, and high-risk outputs. You deploy with approvals, starting in draft or recommendation mode and graduating only low-risk actions after review metrics are strong. Finally you monitor and improve by tracking failures, edits, tool errors, latency, cost, and business outcomes so the agent keeps getting better after launch.

Your instructor
Deep
Deep
ML Architect & Full Stack Engineer

Machine Learning Architect and Full Stack Engineer building practical AI agent workflows for business teams. 10+ years shipping production ML across TensorFlow, PyTorch, AWS, and GCP. Active open-source AI contributor - and the person who ships every A8gent agent before it becomes a lesson.

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Why this course matters

Production AI agent engineering is where agent ideas stop being demos and become operational systems that a business depends on every day. The gap between a prompt that works once and an agent that runs reliably at volume is enormous, and it is filled with reliable inputs, test cases, logs, named owners, retries, and a plan for failures. Most agent projects stall not because the model is weak but because no one designed evaluation, monitoring, permissions, or ownership before shipping. Teams that can engineer agents properly, with observability into every decision and tool call, are the ones that move past pilots into systems people trust. This skill is increasingly what separates an engineer who can prototype from one who can put an agent into production and keep it healthy.

How the course works

01
Design the architecture
Map the full path of a request: trigger, context gathering, model call, tool actions, storage, review, final output, and escalation. Decide where the agent is allowed to act on its own and where a person or a rule must gate it. A clear architecture on paper prevents most of the ad hoc patching that makes agents fragile later.
02
Prepare and govern data sources
Connect documents, CRM records, tickets, forms, spreadsheets, or databases with clear source priority and permission rules. Decide which source wins when two disagree and what the agent does when data is missing. Scope access tightly so the agent can read only what the task needs, and log what it touched.
03
Build the prompt and instruction system
Turn requirements into structured instructions with role, source rules, output schema, worked examples, and explicit boundaries. Version these like code so you can trace which change moved quality up or down. Treat the instruction set as a maintained artifact, not a string someone edits live in production.
04
Add tool integrations with guardrails
Give the agent scoped tools for the specific actions it needs rather than broad system access. Validate inputs and outputs at each tool boundary so a hallucinated argument cannot corrupt data or trigger an unsafe action. Decide which tools are read-only, which need approval, and which are never available.
05
Create evaluations
Build test cases for normal inputs, edge cases, missing data, bad instructions, and high-risk outputs, drawn from real historical work. Score outputs against a rubric so you can compare versions objectively instead of by vibe. A held-out evaluation set is what lets you change a prompt or model without regressing quietly.
06
Handle failures, retries, and timeouts
Design what happens when a tool errors, the model returns malformed output, or a call runs too long. Add retries, fallbacks, and safe stops so a single failure does not cascade or leave a half-finished action. Assume every external call can fail and decide the safe default for each one.
07
Instrument observability and tracing
Log inputs, model decisions, tool calls, outputs, and reviewer edits so you can reconstruct any run after the fact. Capture latency and cost per step so you can find the slow and expensive parts. Without traces you are debugging production agents blind, and small regressions go unnoticed until they are expensive.
08
Deploy with approvals and staged rollout
Start in draft or recommendation mode where the agent proposes and a human confirms, then graduate low-risk actions only after review metrics are strong. Roll out to a subset of traffic before full launch so failures surface on a small blast radius. Keep a fast path to disable or roll back the agent.
09
Monitor and improve after launch
Track failure rate, reviewer edits, tool errors, latency, cost, and the business outcome the agent was built to move. Feed real failures back into the evaluation set so the agent gets more robust over time. Name an owner responsible for the agent when data, tools, prices, or requirements change.
10
Manage cost, latency, and scale
Profile where tokens, calls, and time are spent, and cut waste through prompt trimming, caching, and model selection. Load-test the workflow at expected volume so it does not fall over the first busy day. Balance quality, speed, and cost deliberately rather than accepting whatever the first working version happened to use.

Who this course is for

Engineers, technical leads, AI engineers. If that is you, this course turns the topic into something you can actually ship and run, not just watch.

Mistakes this course helps you avoid

  • Shipping a demo as if it were a production workflow.
  • Skipping evaluations because outputs looked good once.
  • Not logging inputs, outputs, and reviewer edits.
  • Failing to name an owner for maintenance.
  • Granting broad system access instead of scoped, validated tools.
  • Ignoring retries and timeouts until a single failure cascades in production.

Course FAQ

Who should take a production agent engineering course?

It is for engineers, technical operators, automation teams, and agencies responsible for shipping agents into real workflows and keeping them running. It assumes you are comfortable with code and want the practices that make agents reliable at scale.

Do I need to know how to code?

Yes, this is the technical track. You should be comfortable reading and writing code and calling APIs, because production AI agent engineering covers tool integrations, evaluations, tracing, and deployment rather than no-code building.

What should be implemented first?

Start with a reviewed workflow where mistakes are easy to inspect, such as internal research, triage, drafting, or reporting. Getting evaluation, logging, and approvals right on a low-risk agent teaches the patterns you will reuse on higher-stakes ones.

Is this framework-specific?

The practices apply across stacks, whether you build with a framework or direct model APIs. The focus is on architecture, evaluations, observability, and deployment, which matter regardless of the specific library you choose.

How is this different from a beginner agent course?

A beginner course gets one agent working once. This course covers what it takes to run agents in production: evaluation sets, tracing, failure handling, staged rollout, cost control, and ownership. The emphasis is reliability, not the first demo.

How do I know an agent is ready for production?

It passes a real evaluation set including edge cases, it logs every decision and tool call, it has defined failure and escalation paths, and it has a named owner. The course gives you a concrete readiness checklist rather than a gut feeling.

What will I build during the course?

You will engineer at least one agent end to end with scoped tools, an evaluation harness, observability, approval gates, and a staged rollout plan. The goal is a workflow you could defend to a team that has to depend on it.

How much time does it take?

Plan for meaningful hands-on time, since the value is in building evaluations, wiring tracing, and testing failure paths rather than watching lectures. The exact pace depends on how much production agent work you already do.

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