An agentic AI workflow is a process where AI agents autonomously perceive, decide, reason through multi-step tasks, and execute actions with minimal human intervention. Unlike single-shot prompting, agentic workflows place a model inside a loop — it plans, acts, observes results, and iterates until a goal is satisfied.¹⁾

Ng's key insight captures the paradigm shift: «Better workflows beat better models.»

A workflow earns the label agentic when it exhibits all of the following properties:

• Autonomy — the agent selects its next action without step-by-step human direction.
• Iterative reasoning — outputs are refined across multiple passes rather than returned in one shot.
• Tool integration — the agent can invoke external capabilities: web search, code execution, database queries, REST APIs.
• Adaptive planning — if a sub-task fails or new information arrives, the agent revises its plan on the fly.
• Memory — state is preserved across steps (in-context, vector store, or external database) so earlier results inform later decisions.

These properties together allow a small model running inside a well-designed loop to outperform a much larger model running in zero-shot mode — a finding repeatedly confirmed on coding benchmarks (see Benchmarks below).

Andrew Ng identified four foundational design patterns for agentic systems at the Sequoia AI Ascent conference in March 2024.²⁾

The agent critiques its own output and iterates. A separate critic call (which may use the same model) scores or annotates the draft; the generator then revises. This simple loop pushed GPT-4 from 67% to 88% on the HumanEval coding benchmark — matching or exceeding human performance without any change to the underlying model weights.

The agent interleaves Reasoning and Acting steps (the ReAct pattern): it emits a thought, calls a tool (web search, Python interpreter, SQL query, external API), observes the result, and continues reasoning. Tool use breaks the knowledge-cutoff barrier and enables real-time data access.

The agent decomposes a high-level goal into an ordered sequence of sub-tasks, executes them, and replans when a step fails or returns unexpected results. Planning agents can handle objectives that span dozens of tool calls and require backtracking.

Specialised agents collaborate: one orchestrates, others execute domain-specific subtasks (coding, research, QA, summarisation). Systems such as ChatDev model an entire software company as a society of agents with distinct roles. Multi-agent architectures increase parallelism and allow each agent to stay within a focused context window.

Practitioners increasingly treat deliberate human checkpoints as a first-class design element rather than an afterthought. The agent pauses at high-stakes decision points, presents its reasoning, and waits for approval before proceeding. This pattern is especially prominent in enterprise deployments where auditability and compliance are required.

HumanEval pass@1 results illustrate the leverage that agentic scaffolding adds on top of raw model capability:

The AlphaCodium result — achieved by wrapping GPT-4 in a multi-step code-generation and test-driven refinement loop — exceeds the zero-shot score by more than 28 percentage points without fine-tuning.

The following open-source and managed frameworks are widely used to build agentic AI workflows:

Adoption has moved from research to production faster than most technology cycles:

• PwC reports that 79% of organisations are running AI agents in production as of early 2026, up from negligible deployment two years prior.
• AT&T processes 8 billion tokens per day through its internal agentic stack and has achieved a 90% cost reduction compared to earlier approaches — a case study in scaling agent infrastructure to enterprise traffic.³⁾
• Deloitte Tech Trends 2026 cautions that organisations deploying agents at scale must address trust, observability, and governance before expanding agent autonomy further.⁴⁾

The cost-reduction figures from AT&T underline Ng's workflow-over-model thesis: infrastructure and orchestration design matter as much as model selection.

• Agent Orchestration
• ReAct: Reasoning and Acting
• Plan and Execute Agents
• Multi-Agent Systems
• CrewAI
• LangGraph
• AutoGen