Foundations
Technology foundations of AI agents: architectures, protocols (MCP, A2A), reasoning patterns, and foundational papers.
21 resources
The agentic AI landscape and its conceptual foundations
OECD landscape paper mapping agentic AI architectures, capability tiers, and governance touchpoints for policymakers. Synthesises definitions across vendors and academic work into a shared vocabulary, flagging where existing AI policy instruments need adjustment for agents.
AI Index Report 2026 (Technical Performance chapter)
Stanford HAI's annual index chapter on technical performance, tracking benchmark progress for reasoning, coding, and tool-using agents. Covers capability jumps on SWE-bench, GAIA, and WebArena plus compute and cost trends across frontier model families.
The 2026 Guide to AI Agents
IBM explainer introducing AI agent architectures, planning loops, memory, and tool use for enterprise teams. Walks through single-agent, multi-agent, and hierarchical patterns with use cases across IT operations, customer service, and supply chain automation.
A practical guide to building agents
OpenAI handbook on deciding when an agent is appropriate, selecting models, writing clear instructions, defining tools, and adding safety guardrails. Covers single-agent and manager patterns with examples using the OpenAI Agents SDK.
Building Effective AI Agents
Anthropic engineering write-up distinguishing deterministic workflows from agents and documenting composable patterns like prompt chaining, routing, parallelisation, orchestrator-workers, and evaluator-optimiser. Recommends starting with the simplest pattern and adding autonomy only where it pays off.
Agents
Chip Huyen's long-form essay breaking down agent components (planning, tool use, memory, reflection), common failure modes, and evaluation challenges. Covers ReAct-style loops, function calling, and practical trade-offs when moving from prototypes to production agents.
Delegation Nation: Advanced AI Assistants and why they matter
Ada Lovelace Institute policy briefing on advanced AI assistants as systems that act on a user's behalf. Examines concentration of power, delegation risks, and policy levers, calling for UK-specific rules on consent and accountability.
Large Language Model Agents: A Comprehensive Survey on Architectures, Capabilities, and Applications
Academic survey cataloguing LLM agent architectures, planning and reasoning methods, memory mechanisms, tool-use strategies, and multi-agent coordination. Also maps application domains from coding and science to robotics, with open challenges for each layer.
ReAct: Synergizing Reasoning and Acting in Language Models
Yao et al. introduce the ReAct prompting framework that interleaves chain-of-thought reasoning traces with tool actions, letting models plan, act, and observe in a loop. Evaluated on HotpotQA, FEVER, ALFWorld, and WebShop.
Toolformer: Language Models Can Teach Themselves to Use Tools
Meta AI paper showing how a language model can teach itself to call external APIs (calculator, search, translator, calendar, Q&A) through self-supervised fine-tuning on API-augmented data, improving zero-shot performance without losing core skills.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Google Research paper showing that prompting large models with worked-example reasoning chains elicits multi-step arithmetic, commonsense, and symbolic reasoning. Establishes the chain-of-thought technique that underpins most modern agent planning loops.
Model Context Protocol specification
Model Context Protocol 2025-11-25 specification defining the JSON-RPC interface that lets AI applications connect to tools, resources, and prompts through standard servers. Covers transport, authentication, capability negotiation, and progress notifications for agent integrations.
Model Context Protocol reference repository
GitHub monorepo for the Model Context Protocol project containing the specification, schemas, reference SDKs, and example servers. Entry point for developers building MCP-compatible tools, clients, or servers across Python, TypeScript, and other languages.
What is the Model Context Protocol (MCP) and how does it work?
Google Cloud explainer introducing the Model Context Protocol, its client-server architecture, and how MCP servers expose tools and data to agents. Walks through Vertex AI integration patterns and compares MCP with bespoke tool plumbing.
Announcing the Agent2Agent Protocol (A2A): A new era of Agent Interoperability
Google's launch post for Agent2Agent, an open protocol that lets agents built on different stacks discover each other, exchange capabilities, and coordinate long-running tasks. Supported at launch by 50+ partners including Atlassian, Salesforce, and SAP.
AI Index Report 2026: Technical Performance (Chapter 2)
The technical performance chapter of Stanford HAI's 2026 AI Index, tracking how frontier and agentic models perform across reasoning, coding, and autonomy benchmarks, including the rapid rise of agentic task completion over the past year.
What is the Model Context Protocol (MCP)?
The official introduction to the Model Context Protocol, an open standard for connecting AI agents to external tools, data sources, and systems through a common interface. MCP has become a foundational building block for interoperable agentic applications.
AI Agent Systems: Architectures, Applications, and Evaluation
A 2026 survey that organizes AI agent systems into a unified taxonomy across deliberation and reasoning, planning and control, and tool calling and environment interaction. Covers agent components (LLM core, memory, world models, planners, tool routers, critics) and single- vs multi-agent orchestration patterns.
NIST AI Agent Standards Initiative
Launched by NIST’s Center for AI Standards and Innovation (CAISI) in February 2026, the AI Agent Standards Initiative signals that purpose-built standardization for autonomous systems is now a federal priority. It builds on the AI RMF, the Cyber AI Profile, and the COSAiS control overlays for single- and multi-agent deployments.
Donating the Model Context Protocol and establishing the Agentic AI Foundation
Anthropic’s announcement that it is donating the Model Context Protocol to the Linux Foundation’s new Agentic AI Foundation, alongside founding projects such as Block’s goose and OpenAI’s AGENTS.md. Establishes neutral governance for the open standards underpinning interoperable agents.
The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey
An early and widely cited survey of emerging AI agent architectures, comparing single-agent and multi-agent designs and how they implement reasoning, planning, and tool calling. A useful reference point for the design patterns later formalized in 2025–2026 work.