28 operational tools spanning the complete AI Agent Lifecycle — from business case to incident response. Templates, playbooks, workbooks, and blueprints organized by the decisions you face at each stage.
A cross-functional discipline — like Software Engineering or Data Engineering — that spans the full lifecycle of designing, building, governing, and operating AI agent systems in production. It's not one person's job. It's how the entire team works.
The builder — one of six roles that practice Agentic Engineering. Writes prompts, wires tool calls, integrates APIs, and makes the agent actually work. Important, but not the whole picture.
| DIMENSION | SOFTWARE ENGINEERING | AGENTIC ENGINEERING |
|---|---|---|
| What you build | Deterministic applications — same input → same output | Probabilistic agents — same input → different reasoning paths |
| Core loop | Plan → Code → Test → Deploy → Monitor | Justify → Architect → Govern → Build → Gate → Operate |
| Testing | Unit tests, integration tests — pass/fail | Eval suites, red-teaming, behavioral testing — accuracy on a spectrum |
| Security model | Input validation, auth, network boundaries | All of the above + prompt injection, tool permissioning, autonomy tiers, guardrails |
| Failure mode | Crashes, exceptions — visible and predictable | Hallucinations, wrong actions, silent drift — invisible and unpredictable |
| Governance | Code review, CI/CD gates | AI constitution, autonomy spectrum, human-in-the-loop, policy engines |
| Key new concern | — | Governance comes before Build — not after. The 512-vulnerability lesson. |
| Roles | Dev, QA, DevOps, PM, Architect | Product Manager, Architect, Safety Engineer, Agent Engineer, Evaluator, AgentOps |
A streamlined scoring framework for evaluating and ranking AI agent use cases. Score candidates across feasibility, impact, risk, and strategic fit.
A quick-start calculator for estimating the return on investment of an AI agent initiative. Input costs and projected benefits to get a simple payback analysis.
Structured assessment to identify predictable failure modes in your agent project — score by likelihood and impact, then translate results into concrete mitigations.
Architecture pre-flight checklist covering reliability safeguards, operational readiness, governance alignment, and core design choices — 95 checkpoints across 12 domains.
Evaluate your organization's readiness for AI agent adoption — stakeholder alignment, skill gaps, and change management risks.
Practitioner-friendly workbook covering reasoning mechanisms, agent patterns, reflection & self-correction, and memory integration — with side-by-side comparison.
Side-by-side matrix of agent frameworks — LangChain, LangGraph, Autogen, Semantic Kernel — filterable by tool integration, memory, and enterprise readiness.
Reference architecture for a production-ready multi-agent platform — request flow, orchestration layer, agent registry, secure message bus, and observability.
Dual-playbook: Implementation Playbook for readiness and controlled deployment, plus AgentOps Operational Playbook for continuous oversight.
System prompt templates, few-shot libraries, and context engineering patterns for production agent systems.
Design your agent's knowledge layer — RAG vs. CAG vs. KAG architecture, chunking strategies, and memory tier decisions.
Structured registry for every tool and API your agents can call — allow-lists, rate limits, permission boundaries.
The 'constitution' for how AI agents are built, deployed, and managed — roles, decision rights, autonomy tiers, human oversight, logging, and compliance.
Excel-based governance tracker and centralized agent registry — agent registry, credentials & roles, risk profiles, controls checklist, and policy log.
When your agent goes rogue at 2 AM — severity classification, containment procedures, communication templates, and post-incident review protocols.
Define when humans approve, monitor, or trust fully — escalation thresholds, review workflows, and approval chains.
Field-level data classification for agent inputs and outputs — PII handling rules, sensitivity tiers, data flow mapping.
Bias evaluation, fairness benchmarks, and explainability requirements — produces a Responsible AI stamp for each agent.
Patterns for multi-agent communication, delegation, shared state management, conflict resolution, and end-to-end integration testing.
Test suite templates, baseline KPI definitions, red-teaming scenarios, and regression testing for agent behavior.
Token cost projections, infrastructure scaling curves, and budget-vs-actual tracking for agent operations.
The 7 non-negotiable AgentOps items as a go/no-go gate before production deployment.
Phased rollout plan — shadow mode → canary → monitored production — with rollback criteria and success thresholds.
KPI definitions, alert thresholds, and drift detection rules for continuous agent monitoring.
Change history tracker, lessons learned registry, and feedback loop back to Phase 1 anti-patterns.
Agent fleet inventory — track all agents across the organization with health status and governance posture.
Model capability matrix, pricing comparison, and provider lock-in assessment for foundation model selection.
Regulation-to-control matrix across EU AI Act, NIST AI RMF, SOC 2, GDPR, HIPAA — with gap analysis.
The mental model — cognitive loop, MCP, Skills, memory, guardrails. The noise filter for evaluating every agent product and framework you encounter.
The tool selection discipline — problem first, tool second. 30+ tools across 7 layers, but the sequence matters more than the speed.
The operational playbooks — 28 tools across 6 lifecycle stages. Governance before build. Use case validation before code. Discipline over speed.
The mental model is the noise filter. The tool selection discipline keeps you honest. The operational playbooks make execution systematic. All three live on this platform.
We covered the Primer, the Builder's Guide, and the Use Case Discovery Workbook today. On March 25th, we go deeper — governance templates, architecture blueprints, and the operational playbooks for Stages 2 through 6.