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Agent Frameworks and How Agents Fail

Frameworks such as LangGraph, CrewAI and AutoGen give you state, checkpointing and tracing, which is most of what a production agent needs and none of what makes one work. The failure modes they do not fix are the ones that end pilots: loops that never terminate, budgets that blow, and tool-selection errors that compound quietly.

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RELATED CONCEPTS

The ReAct Loop (Reason, Act, Observe)Agent vs Workflow vs a Single Call Agentic Evals: Grading the Trajectory, Not the Answer Bounded Autonomy and Human-in-the-Loop Observability for AI Systems

LESSONS THAT TEACH THIS

The FDE EngagementRecover partial runs without guessing whether an action happened30 min→Hard DeploymentsTwo hundred agents making the same mistake is one incident, not two hundred30 min→

PRACTICE THIS IN REAL QUESTIONS

RAG & Agent System DesignWhen is an agent the wrong answer? Argue against building one.→AI Security, Privacy & GovernanceWalk me through the OWASP LLM Top 10, which two risks would you prioritize for an enterprise agent deployment?→AI Security, Privacy & GovernanceWhat is 'excessive agency' in an agentic system, and what controls actually limit it?→RAG & Agent System DesignDesign a customer-support agent with order-status and returns tools. How do you keep it safe and useful?→AI Security, Privacy & GovernanceHow do tool poisoning and prompt injection apply to MCP servers and agentic AI, and how do you defend them?→RAG & Agent System DesignA brand wants an agent that never gives financial advice, stays on-voice, and never mentions competitors. Design the guardrails.→