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1. User Request The process begins when a user submits a request to the AI system. This request could be a question, instruction, or task that the user wants the system to perform. At this stage, the request represents the user's intent, but it has not yet be...

Many failures are context failures, not model failures. This remains true, and is now the organizing principle of the discipline described in Section 5. Context Builder Responsibilities Assemble: ●       Relevant memory ●       Retrieved evidence ●       Tool ...

NEW IN 2026  Agent memory matured from "pick a vector database" into a benchmarked production discipline in 2026, with dedicated evaluation suites (LoCoMo (Long Conversation Memory)), MemBench, MemoryAgentBench, MemoryArena) and an ecosystem spanning roughly 2...

Traditional RAG: retrieve → answer Modern Agentic RAG: retrieve → compare sources → detect conflicts → score trust → reason over evidence → answer with citations NEW IN 2026 Agentic RAG is now commonly framed as the evolution of RAG into a "context engine": th...

Use standardized tool interfaces where possible. MCP is now the dominant standard for this layer, adopted beyond Anthropic by OpenAI, Google DeepMind, Microsoft, and thousands of teams, with roughly 97 million monthly SDK downloads and 19,000+ servers indexed ...

Agent Skills Layer  NEW IN 2026  Skills are an emerging complement to MCP tools: composed, reusable capability bundles (instructions + optional scripts/resources) that an agent loads on demand, rather than a live tool connection. The MCP roadmap lists a formal...

1. Agent Wants an Action The process begins when the AI agent determines that it needs to perform an action to complete a task. This could involve accessing data, sending a message, updating a system, triggering a workflow, or interacting with an external app...

Use role-based agents instead of one omnipotent agent. Agent Responsibility Planner Break down tasks Researcher Retrieval Executor Tool use Verifier Check outputs Safety Agent Policy checks Finance Agent Budget contro...

Threats ●       Prompt injection ●       Tool injection ●       Retrieval poisoning ●       Memory poisoning ●       Privilege escalation ●       Hidden instructions in documents ●       Fake knowledge sources MCP-Specific Threats ●       Tool rug-pull — a con...

When failures occur: ●       Retry ●       Alternate tool ●       Re-plan task ●       Ask clarifying question ●       Human escalation ●       Graceful degradation Operational Modes ●       Normal Mode ●       Low Cost Mode ●       High Accuracy Mode ●       ...

Track every step. Required Telemetry ●       User request ●       Context loaded ●       Retrieved documents ●       Tool / MCP calls (including which server and scope) ●       Policy decisions ●       Model chosen ●       Tokens used ●       Cost ●       Late...

Test continuously in simulation. Benchmark Categories ●       Multi-step tasks ●       Tool calling accuracy ●       Hallucination rate ●       Prompt injection resistance ●       Recovery after outage ●       Cost per successful task ●       Approval accuracy...

Before Launch ●       Secrets in vault ●       Rate limiting ●       Retry logic ●       Queueing ●       Structured logs ●       Health checks ●       DB pooling ●       CI/CD ●       Rollback strategy ●       Human approval console ●       Policy engine live...

Layer Choice API FastAPI Agent Framework LlamaIndex Workflow LlamaIndex Workflows Tool Interoperability MCP (2026-07-28 spec, stateless) Vector Memory ChromaDB Graph Memory Neo4j Agent Memory Layer Mem0 / Zep (...

 What to Use When Scenario When to use Highest transparency ReAct + logs Fastest cheap execution Small model routing Complex workflows Multi-agent workflow / task graph Safety critical Verifier + human gate Large enterprise m...

Build in This Order ●       Single agent + retrieval ●       Add memory (start with Pattern B — retrieval-augmented) ●       Add tools via MCP ●       Add policy engine ●       Add verifier ●       Add human approvals ●       Add multi-agent roles ●       Opti...