An Agentic First Approach is a way of designing projects where the default unit of work is not a document, a meeting, or a ticket, but an *agent* that can read context, make a plan, take actions, and report outcomes. The point is not to replace humans, but to treat AI agents as first-class participants in the workflow, with clear roles, permissions, and feedback loops, so that the system can move quickly while remaining legible and safe.
# Why “agentic” is different A normal AI-assisted workflow treats the model as a clever autocomplete. An agentic workflow treats the model as a goal-directed actor operating inside a bounded environment. The difference is the presence of a loop: observe, decide, act, verify, and then either finish or iterate. This loop is what turns “chat” into “work”, and it is why agentic systems need structure, constraints, and auditability.
# Typical structure Most Agentic First systems converge on a small set of repeating components. - A Goal: a clear outcome, stated in a way an agent can evaluate. - A Context Pack: the minimal source materials the agent is allowed to use, often curated to avoid hallucination and leakage. - A Plan: a step sequence that the agent writes, that can be reviewed, and that can be re-run. - A Toolbelt: permitted actions such as file read/write, web research, calendar lookup, code execution, or API calls. - A Memory: what the agent is allowed to retain between runs, and what must be forgotten. - A Verification Loop: tests, checks, citations, diffs, or human review gates. - A Report: a short narrative of what changed, why, and what remains uncertain. The most important design choice is that every action is either inside the toolbelt, or it does not happen.
# Common patterns Agentic First work is often structured into a few standard patterns that make projects feel calm rather than chaotic. - Planner–Executor: one agent writes a plan, another carries it out, which reduces impulsive tool use. - Research–Synthesis: a researcher agent gathers sources and citations, a writer agent produces the deliverable. - Critic–Builder: a critic agent attacks the draft or implementation, the builder responds with changes. - Human-in-the-loop Gates: the agent can draft and propose, but must pause at specific checkpoints for approval. These patterns are less about hierarchy and more about separating failure modes.
# The minimum viable agent loop A practical agent loop can be expressed as five steps. - Read the goal and constraints. - Load the allowed context. - Propose a plan and success criteria. - Execute actions with logs and reversible changes. - Verify, summarise, and produce outputs. When this loop is stable, you can scale it horizontally by adding more agents or more tasks without losing control of the system.
# How teams implement it Many teams implement Agentic First by creating a small “agent operating system” around their existing work. - A repository of prompts, roles, and runbooks. - A standard context format, often a folder or “brief” file. - A consistent way to grant tools and permissions. - A test or review layer that makes outputs trustworthy. - A changelog mindset: every run produces a diff, a citation list, or a trace. The goal is that a new agent can join the work the way a new human can, by reading the runbook.
# Benefits and risks Agentic First tends to produce speed, parallelism, and better reuse of process knowledge, because the work becomes repeatable. The risks are also predictable: agents can act too confidently, use the wrong context, or optimise for completion over correctness. This is why the approach only works when bounded by permissions, verification, and a culture of “show your working” through diffs, tests, citations, and logs.