Coverage for product, engineering, and AI platform teams deciding how to build, evaluate, secure, and operate agent systems.
Builder-first curriculum
Start with what AI agents are, move into real examples and use-case selection, then dig into architecture, specialist-role design, frameworks, protocols, orchestration, security, and evaluation. The guide library is ordered to help technical teams build context in the right sequence.
12
foundational guides already stitched into the core reading sequence
5
foundations, pilot selection, system design, stack or protocol choices, and launch controls
2
topics queued after the current starter set
Guide coverage
Foundations
Agent News Watch for teams building and operating AI agents.
Foundations
Learn what AI agents are, how they work, how they differ from chatbots and copilots, and where they fit in real production workflows.
Learning path
This sequence defines AI agents, studies examples, scores first-pilot use cases, maps architecture, covers specialist-role patterns, walks through implementation, compares frameworks, clarifies protocols, coordinates workflows, hardens security, and measures quality in production.
Step 1: Start here
Guide coverage
Foundations
Agent News Watch for teams building and operating AI agents.
Learn what AI agents are, how they work, how they differ from chatbots and copilots, and where they fit in real production workflows.
Step 2: See real workflows
Guide coverage
Foundations
Agent News Watch for teams building and operating AI agents.
Explore concrete AI agent examples across coding, research, support, operations, sales, and personal productivity, with tools, autonomy level, and build lessons.
Step 3: Choose the first pilot
Guide coverage
Foundations / Implementation
Agent News Watch for teams building and operating AI agents.
Learn the best AI agent use cases for product, ops, engineering, and support teams, plus how to choose the right autonomy level, architecture, and rollout path.
Step 4: Map the system
Guide coverage
Architecture
Agent News Watch for teams building and operating AI agents.
Learn how AI agent architecture works across models, tools, memory, orchestration, guardrails, and multi-agent patterns with practical reference designs.
Step 5: Build your first system
Guide coverage
Implementation
Agent News Watch for teams building and operating AI agents.
Learn how to build AI agents step by step, from task selection and tool design to memory, guardrails, testing, and production rollout.
Step 6: Choose your stack
Guide coverage
Frameworks
Agent News Watch for teams building and operating AI agents.
Compare AI agent frameworks, understand when you need one, and learn how to choose the right stack for workflows, coding agents, and multi-agent systems.
Step 7: Standardize tools and context
Guide coverage
Protocols
Agent News Watch for teams building and operating AI agents.
Learn what Model Context Protocol is, how MCP clients and servers work, and when it beats bespoke tool integrations for AI agents.
Step 8: Handle cross-agent handoffs
Guide coverage
Protocols
Agent News Watch for teams building and operating AI agents.
Learn what Agent-to-Agent Protocol is, how A2A handles cross-agent communication, and when builders should care about A2A versus MCP.
Step 9: Coordinate workflows safely
Guide coverage
Implementation
Agent News Watch for teams building and operating AI agents.
Learn AI agent orchestration patterns for coordinating state, tools, retries, approvals, and multi-step workflows without overbuilding your stack.
Step 10: Split work across specialists
Guide coverage
Architecture
Agent News Watch for teams building and operating AI agents.
Learn when multi-agent architecture outperforms single-agent systems, which coordination patterns fit best, and how to manage context, reliability, security, and cost.
Step 11: Lock down the system
Guide coverage
Security
Agent News Watch for teams building and operating AI agents.
Learn how to secure AI agents against prompt injection, over-permissioned tools, unsafe memory, insecure handoffs, and risky outputs with practical controls.
Step 12: Measure quality before scale
Guide coverage
Evaluation
Agent News Watch for teams building and operating AI agents.
Learn how to evaluate AI agents with task-based evals, regression checks, human review, and production metrics across tools, safety, latency, and cost.
Topic map
Jump into the right lane instead of treating the guide library like a flat archive: foundations, first-pilot use cases, single versus multi-agent design, stack and protocol choices, plus the launch controls that keep the system governable.
Use the foundations lane to separate actual agents from chat or workflow theater, then study the concrete jobs teams already automate well.
Use this lane before you score pilots, split roles, or shop stacks.
Turn examples into a real first sprint by scoring value, autonomy, risk, approvals, and the implementation path behind each candidate workflow.
Best entry point when the question is what your team should test next, not what an agent is.
Define the base architecture, decide when one agent is enough, and add orchestration or specialist roles only when the workflow gets clearer.
Use this lane after the pilot is real enough to own state, retries, approvals, and handoffs.
Compare frameworks, standardize capability access, and separate tool calls from cross-agent delegation when the runtime starts to widen.
This lane matters when implementation surfaces, not just concepts, become the real blocker.
Keep rollout discipline attached to the build by tightening tool access, approval boundaries, quality checks, and incident response before autonomy expands.
The strongest stacks still fail without security, measurement, and stop conditions.
Start with the numbered path if you are ramping up. Use the cluster map if you are solving a narrower architecture or operations problem.
The latest curriculum updates now cover first-pilot selection and the point where a workflow should split across specialist roles.
MCP Security, Best AI Agent Frameworks.