9 Agentic AI Concepts
Everyone Must Know

An LLM with no memory is a stateless function — it takes an input, produces an output, and forgets everything. An agent with memory is something qualitatively different: it can learn from past interactions, build context across a multi-step workflow, track progress toward a goal, and personalise its behaviour based on accumulated knowledge of a user or domain. Memory is what turns a query-response loop into an intelligent autonomous actor.

Memory operates at four levels. In-context (working) memory lives within the active context window — fast but bounded and ephemeral. External retrieval memory lives in vector databases, knowledge graphs, or document stores — retrieved dynamically via RAG. Episodic memory stores structured summaries of past conversations, distilled to preserve meaning across context resets. Semantic memory stores generalised facts the agent has learned, independent of any specific conversation.

State captures the agent’s current operational snapshot — where it is in a workflow, what tools it has invoked, and what information is immediately available. In production agentic systems, state is externalised from the model into a durable state store (Redis, a database, or a workflow orchestration system) so that long-running tasks survive context resets, model crashes, and multi-session continuity requirements.

Orchestration is the coordination layer that decides which agent does what, in what order, with what inputs — and what to do when something fails. In a single-agent system, orchestration is simple: a loop that runs plan-act-observe until the task is done. In a multi-agent system, orchestration is a distributed systems problem: routing tasks to specialist agents, managing parallelism, resolving conflicts, enforcing policies, and synthesising results.

The dominant orchestration topologies in production 2026 systems are: hierarchical (a coordinator agent manages specialist agents in a tree structure — clearest governance, most control); peer-to-peer (agents negotiate task delegation directly via A2A — most flexible, hardest to audit); and pipeline (agents process work sequentially, each passing output to the next — simplest, brittle if one stage fails).

Leading frameworks include LangGraph (graph-based state machine, best for complex stateful workflows), CrewAI (role-based team orchestration with natural language task delegation), and AutoGen (conversational multi-agent patterns with flexible topologies). The 2025–2026 trend, driven by the increasing reasoning capability of frontier models, is away from heavy external orchestration toward model-native reasoning — letting the model itself decide when to call tools, delegate, or iterate, with the orchestration framework handling state and governance rather than decision logic.

LLMs know only what was in their training data — a snapshot that is always months old and often wrong for specialist domains. RAG gives agents access to current, domain-specific, trusted knowledge without retraining, by retrieving relevant information at query time and injecting it into the context window as grounding for generation.

In 2026, RAG has evolved into Agentic RAG — where the agent dynamically decides when to retrieve, what to retrieve, and whether to iterate. Rather than a fixed single-pass retrieval, an agentic RAG system can issue multiple targeted queries, evaluate retrieved chunks for relevance, self-check its generated answer against sources, and loop until it is confident the answer is grounded. The production default stack for 2026 is LangGraph for orchestration, LlamaIndex Workflows for retrieval, and Ragas + Phoenix + Langfuse for evaluation — targeting faithfulness ≥ 0.90 and answer relevancy ≥ 0.85.

RAG also serves as the primary mechanism for long-term memory: rather than stuffing all prior context into the context window (which quickly overflows), agents store episodic summaries and structured facts in a vector store and retrieve only what is needed for the current task.

An LLM alone is a reasoning machine — it produces text. An agent harness is the programmatic infrastructure that wraps that reasoning machine and makes it an actor: providing context, managing permissions, executing tool calls, persisting state, and enforcing safety. The equation is Agent = LLM + Harness.

The harness is the layer that 2026’s most experienced practitioners call harness engineering — a shift in focus from prompting the model to making failures structurally impossible at the infrastructure level. The LLM is treated as a frozen reasoning calculator; the harness governs everything around it.

The harness’s most critical design rule: never let the LLM call tools directly. Instead, the model returns a structured tool-call specification; the harness validates the schema, checks permissions against a risk taxonomy (read-only vs. financial vs. destructive operations), executes, and injects the structured result back into the context. This prevents prompt injection from escalating to arbitrary code execution — a real production security risk.

Context compaction is the harness’s answer to context overflow — the most common cause of long-horizon agent failure. As the context window fills, the harness applies progressive disclosure: summarising resolved sub-tasks, evicting low-relevance information, and compressing completed tool call chains into structured summaries, while preserving the information needed for the current step. This extends effective working memory far beyond the model’s nominal context limit.

If you cannot measure your agent’s performance, you cannot improve it — and you cannot trust it in production. Evals (short for evaluations) are the disciplined practice of comparing an agent’s actual outputs against expected outcomes across a representative test set, producing quantitative metrics that track quality over time and alert teams to regressions.

The core eval dimensions for agentic RAG systems in 2026 target: faithfulness (≥ 0.90 — does the output accurately reflect retrieved sources?), answer relevancy (≥ 0.85 — does the output address what was asked?), and context precision (≥ 0.80 — did retrieval return the right chunks?). Beyond these, agent-specific evals measure task completion rate, tool call accuracy, reasoning chain coherence, and safety compliance.

The most scalable approach to test set creation is synthetic evaluation data generation: tools like Ragas’s TestsetGenerator automatically create Q&A pairs from your document corpus, compressing what would be days of manual labelling into minutes. For agent traces, libraries like TruLens and Arize Phoenix instrument reasoning steps and measure outcomes across complex multi-step workflows — enabling root-cause analysis of failures rather than just aggregate pass/fail scores.

Before MCP, connecting an AI agent to a new tool — a database, an API, a file system — required custom integration code for every pairing. A team adding a new tool faced a multi-day engineering task. The Model Context Protocol standardises this interface: any agent (MCP client) can connect to any tool or data source (MCP server) through a single, vendor-neutral protocol — reducing tool integration from days to under an hour.

Created by Anthropic in November 2024 and donated to the Linux Foundation’s Agentic AI Foundation (AAIF) in December 2025, MCP reached 97 million monthly SDK downloads by February 2026 and was adopted by every major AI provider: OpenAI, Google, Microsoft, Amazon, and Anthropic. With 75+ official server connectors and a rapidly growing ecosystem of community-built servers, it has become the HTTP of AI tool access — the universal standard that makes agent-to-tool integration composable rather than bespoke.

A skill is a packaged, reusable capability that an agent can invoke as a named operation. Where an MCP tool exposes a raw function (query this database, call this API), a skill encodes a higher-level behaviour: “draft a summary in this specific style,” “evaluate a contract against this playbook,” “search the knowledge base and return structured citations.” Skills are to agents what libraries are to software engineers — composable, versioned, and testable units of capability.

In the A2A protocol, skills are surfaced in an agent’s Agent Card — the discovery manifest that tells other agents what this agent can do, what inputs it accepts, what authentication it requires, and what output format it produces. When an orchestrator agent needs to delegate a task, it queries the agent registry for agents with the relevant skills and routes accordingly. Skills are the vocabulary of inter-agent negotiation.

Effective skill design follows three principles: single responsibility (each skill does one thing well), schema-defined interfaces (typed inputs and outputs that enable validation), and versioning (skills evolve independently of the agents that use them, with backward-compatible API changes). In enterprise settings, skills are often centralised in a skill library governed by a platform team, with individual agent teams composing from that library rather than reimplementing common capabilities.

Every proprietary orchestration framework solves multi-agent coordination within its own ecosystem — but leaves an open question: how do agents built by different teams, on different frameworks, from different vendors, coordinate without bespoke integration code for every pairing? The A2A protocol is the answer: an open standard for peer-to-peer agent communication, capability discovery, and task delegation.

Introduced by Google in April 2025 and donated to the Linux Foundation in June 2025, A2A absorbed IBM’s competing Agent Communication Protocol (ACP) in August 2025. A2A v1.0, reaching production-ready status in January 2026, introduced cryptographically signed Agent Cards — eliminating the impersonation risk of earlier versions where any agent could claim any capability. With 50+ enterprise technology partners including AWS, Microsoft, Salesforce, SAP, and ServiceNow at launch, A2A has rapidly become the interoperability standard for cross-vendor multi-agent systems.

“If MCP is the wrench for tool access, then A2A is the mechanics’ dialogue — the protocol through which agents coordinate, delegate, and collaborate on shared goals.”

— Web of Agents Research, arXiv:2507.10644, 2026

A multi-agent system (MAS) is a network of specialised AI agents that collaboratively solve tasks too complex or broad for any single agent to handle well. Each agent is a domain specialist — a research agent, a coding agent, a compliance agent, a financial analysis agent — with its own skills, memory, tools, and LLM configuration. The system composes their outputs into a coherent result that exceeds what any individual agent could achieve.

The architectural foundations of a production MAS are: standardised message formats for tasks, context, and responses (A2A provides this); exposed, discoverable endpoints so agents can call each other without bespoke integration (Agent Cards provide this); shared memory or a state synchronisation layer to prevent definition conflicts across agents; and a governance layer that enforces policies, logs decisions, and maintains human oversight for high-stakes actions.

The 28% of enterprises that Innoflexion Research (2026) identifies as successfully scaling multi-agent systems share three practices: MCP + A2A as their interoperability foundation, a three-layer memory architecture (working context + episodic summaries + long-term vector store), and continuous eval pipelines that measure agent quality in production. Those that have failed are doing so due to governance gaps — not model quality. The bottleneck in 2026 is not the intelligence of individual agents. It is the infrastructure for making them trust each other.