Agentic AI

Updated Agent Architecture with Google Cloud's four-component model (persona, memory, tools, model), surface vs. background agent distinction, single vs. multi-agent architecture comparison, and expanded architectural diagram.

Updated Apr 29, 2026

Agent orchestration is the coordination and management layer that schedules, monitors, and governs multiple AI agents and their workflows to ensure coherent progress toward a shared goal.

Updated Apr 29, 2026

Updated Agentic AI page with Google Cloud's six-capability model, component architecture (persona/memory/tools/model), agent taxonomy by interaction mode and agent count, six use-case categories, and expanded comparison with AI assistants and bots.

Updated Apr 29, 2026

Updated the AI Safety and Alignment page with IBM-sourced content on reward misalignment failure modes, cascading multi-agent failures, transparency limitations, and agent identity management, with new links to Agent Orchestration.

Updated Apr 29, 2026

Episodic memory is the agent memory tier that records concrete, timestamped experiences across sessions, enabling cross-session continuity, pattern detection, and failure avoidance in long-running autonomous systems.

Updated Apr 29, 2026

Google DeepMind is Alphabet's primary AI research and product organization, responsible for the Gemini model family and the Vertex AI agent platform, including the ADK and A2A Protocol for multi-agent coordination.

Updated Apr 29, 2026

Grounding is the practice of connecting an AI agent's reasoning to verifiable external information sources to reduce hallucination and ensure outputs are factually accurate and contextually relevant.

Updated Apr 29, 2026

Human-AI collaboration describes workflows in which human workers and AI agents divide labor according to their respective strengths, with research showing that pairing quality — including agent personality design — significantly shapes productivity outcomes.

Updated Apr 29, 2026

John Horton is an MIT Sloan economist who studies AI agents as autonomous economic actors, framing their value in terms of transaction cost reduction and improved decision quality in information-asymmetric markets.

Updated Apr 29, 2026

Kate Kellogg is an MIT Sloan professor whose research examines organizational governance, accountability, and implementation challenges of deploying agentic AI systems in knowledge-work settings.

Updated Apr 29, 2026

Updated Memory Systems with the write-manage-read loop framing, four temporal scopes (working/episodic/semantic/procedural), five mechanism families, comprehensive failure mode taxonomy, design tensions, and practical builder guidance from arxiv 2603.07670 and the Lawson practitioner account.

Updated Apr 29, 2026

The MIT Initiative on the Digital Economy is a research center at MIT Sloan directed by Sinan Aral that produces leading empirical and theoretical work on the economic and societal implications of agentic AI.

Updated Apr 29, 2026

Updated Multi-Agent Coordination with Google Cloud's framing of consensus memory, heterogeneous model support per agent, emergent behavior, the A2A Protocol, and concrete use cases including the Agentic SOC pattern.

Updated Apr 29, 2026

Nicholaus Lawson is a Solution Architect and practitioner-writer who builds production multi-agent systems and has contributed practically-grounded analysis mapping formal academic memory taxonomy to real deployment experience.

Updated Apr 29, 2026

Procedural memory is the agent memory tier encoding behavioral patterns, persona constraints, and operational heuristics loaded at session start, which should be dynamically updated through feedback but is most commonly treated as static configuration.

Updated Apr 29, 2026

ReAct is an agent architecture that interleaves chain-of-thought reasoning traces with tool-calling action steps in a unified context window, enabling adaptive, interpretable multi-step task execution.

Updated Apr 29, 2026

A scratchpad is the ephemeral working memory space in an agent's context window where intermediate reasoning steps and partial results are written and read during a single task.

Updated Apr 29, 2026

Semantic memory is the agent memory tier that stores curated, durable, and abstracted knowledge distilled from experience, with curation being essential to prevent it from degrading into noise or encoding self-reinforcing errors.

Updated Apr 29, 2026

Sinan Aral is the David Austin Professor at MIT Sloan and director of the MIT Initiative on the Digital Economy, whose research examines productivity, governance, and societal implications of agentic AI.

Updated Apr 29, 2026

Summarization drift is a memory failure mode where iterative compression of agent interaction history progressively discards detail, causing the agent's belief state to diverge from what actually occurred.

Updated Apr 29, 2026

Updated Tool Use page with Google Cloud's four-component framework context, interface type taxonomy (physical, graphical, program-based), tool learning concept, expanded use-case examples across six agent categories, and new links to ReAct Framework and Memory Systems.

Updated Apr 29, 2026

The Write-Manage-Read Loop is the three-phase operational model for agent memory covering information ingestion, active maintenance, and retrieval, with the neglected Manage phase being the primary cause of silent agent memory degradation.

Updated Apr 29, 2026