A practical engineering path for building production AI agents, covering ReAct, tool use, memory, multi-agent orchestration, observability, safety boundaries, self-driving codebases, Agent Shepherd workflows, and review gates for reliable autonomous systems.
Learn how to build a production AI agent with typed tools, durable state, guardrails, human approval, tracing, and outcome evaluation. This architecture guide includes a runnable Go runtime, framework selection criteria, security boundaries, and a deployment checklist.
A production-oriented guide to multi-agent systems: when multiple agents are justified, manager-worker and peer architectures, explicit message contracts, least-privilege tools, bounded parallel execution, framework selection, evaluation, and a runnable Go coordinator.
Build a current CrewAI workflow with JSON-first agents and tasks, understand sequential versus hierarchical processes, wrap crews in a Flow for production control, constrain tools and delegation, verify outputs, and evaluate whether multiple agents outperform a simpler baseline.
Compare LangGraph 1.x, Microsoft AutoGen, Microsoft Agent Framework 1.0, and AG2 1.x by lifecycle, state, orchestration, recovery, migration cost, and production risk.
Map the open-source AI agent ecosystem by responsibility rather than popularity. Compare direct model loops, agent SDKs, graph runtimes, role-based orchestration, MCP integrations, evaluation, and deployment through control ownership, task fit, failure recovery, portability, governance, and exit cost.
Understand the ReAct agent pattern as a reasoning-action control loop, not a software framework. Build structured tool calls, policy gates, bounded observations, side-effect recovery, and trajectory evaluations without treating visible reasoning as audit evidence.
Design AI agent memory as a governed lifecycle rather than a vector database. Separate thread state, semantic facts, episodic evidence, and procedural knowledge; implement consent-aware writes, temporal updates, conflict resolution, secure retrieval, deletion, and LongMemEval-style evaluation.
Build reliable Claude Code workflows across the terminal, Agent SDK, and GitHub Actions. Learn task contracts, permission boundaries, session control, isolation, credential handling, deterministic validation, and release gates for production coding agents.
A vendor-neutral architecture guide to cloud coding agents: design per-run identity, isolated workspaces, egress and secret boundaries, branch-scoped delivery, tamper-evident evidence, acceptance gates, cancellation and recovery, and a Go policy validator for safe remote execution.
Build Computer Use agents with reliable observation, browser and desktop actions, approval checks, outcome reconciliation, and practical recovery patterns in Go.
Compare 2026 AI agent frameworks by lifecycle, state, recovery, language support, tool governance, migration cost, and production risk across active runtimes.
Build GitHub Agentic Workflows without handing repository authority to a model. Learn the public-preview Markdown and compiled lock-file model, read-only permissions, safe outputs, credential isolation, prompt-injection controls, deterministic quality gates, OIDC deployment boundaries, cost caps, observability, and evaluation for issue triage, CI investigation, documentation, testing, and pull-request automation.
Deploy enterprise AI agents with qualified workflows, delegated authority, durable state, trace evaluation, cost controls, human escalation, and rollback gates.
A production governance guide for self-driving codebases and agent-authored pull requests. Learn how to classify eligible tasks, choose an autonomy level, define executable acceptance contracts, collect review evidence, restrict credentials and network access, protect reviewer capacity, measure accepted goodput, and roll back unsafe changes without treating vendor adoption claims as universal benchmarks.
A practical engineering guide to A2UI-style Agent-to-UI contracts. Learn how to pin a protocol version, treat generated UI payloads as untrusted data, constrain a component catalog and renderer, authorize every server-side action, protect users from injection and phishing, and test accessibility, retries, privacy, and rollback before production.
A practical comparison of A2UI-style UI contracts, AG-UI-style event protocols, and framework-owned AI UI runtimes. Evaluate payload contracts, transport, rendering, trust boundaries, action authorization, accessibility, recovery, and upgrade risk without treating evolving packages or model output as production guarantees.
Move AI agents from POC to production with workload contracts, versioned evals, shadow traffic, canary gates, rollback drills, SLOs, and incident ownership.
Design durable AI agent state with explicit recovery contracts, atomic checkpoints, idempotent side effects, schema migration, concurrency control, and failure tests.
Learn multi-agent orchestration patterns for production: pipelines, supervisors, handoffs, hierarchies, control contracts, failure handling, and Go code.
Design an observability system for production AI agents that explains what happened without collecting hidden chain-of-thought or unnecessary personal data. This guide defines an event contract, OpenTelemetry boundaries, cost and quality signals, offline and online evaluation, replay-safe debugging, sampling, retention, and failure tests.
A production-oriented guide to Loop Engineering: design triggers, durable state, bounded agent execution, independent verification, budgets, stop conditions, recovery, and human escalation without turning repeated prompts into uncontrolled automation.
Understand an Agent Loop as a production runtime protocol: typed decisions, parallel tool calls, stopping rules, durable state, recovery, and evaluation.
Distinguish an Agent Loop inside one invocation from Loop Engineering across repeated agent runs. Learn the contracts for triggers, goals, state, verification, stopping, escalation, idempotency, and release evidence, with a runnable Go controller that keeps model execution separate from acceptance.
Design AI Agent observability around bounded event contracts rather than raw reasoning capture. This guide separates traces, evaluation, and cost accounting, shows what to redact and hash, explains LLM-as-a-Judge limits, and provides a workload-specific rollout plan for debugging, regression detection, privacy, and budget control.