Quick answer: Agentic coding is when an AI tool does not just suggest code but actively reads your files, writes changes, runs commands, reads the output, and fixes its own mistakes in a loop. Tools like Claude Code and Cursor's agent mode work this way. The difference from autocomplete is that an agentic tool takes actions and reacts to results, rather than producing one snippet and stopping.
I have watched a coding agent take a failing test, read the error, edit three files, rerun the test, and get it green without me touching the keyboard. That is the whole idea. It is also where the honest limits live, so let me walk through both.
Agentic coding vs autocomplete
The old model was completion: you type, the model predicts the next few lines. Useful, but it never leaves the editor and never checks whether its suggestion actually works.
Agentic coding adds a loop and a set of tools. The agent can list files, read them, write to them, run a shell command, and read what that command printed. Then it decides what to do next based on what it saw. That feedback loop is the whole difference.
| Autocomplete | Agentic coding | |
|---|---|---|
| Scope | Next few lines | A whole task across files |
| Feedback | None | Reads command and test output |
| Actions | Suggests text | Edits files, runs commands |
| You | Accept or reject each line | Describe the goal, review the result |
How a coding agent works
Under the hood, an agentic coding tool is a model wired to three things: a set of tools, a loop, and context about your repo. Our post on what an AI agent is covers the general pattern, and coding agents are a focused version of it.
Tools. The agent gets functions it can call: read a file, write a file, search the codebase, run a shell command. Each call returns a result the model can read.
The loop. The agent calls the model, the model asks to run a tool, the tool runs, the result goes back to the model, and it decides the next move. This repeats until the task is done or the agent gives up. Running the tests, seeing them fail, and trying again all happen inside this loop.
Repo context. The agent needs to know your code to be useful. It reads relevant files, searches for symbols, and often keeps a running summary of what it has learned. Good agents pull in only what they need, because context is limited and stuffing the whole repo in wastes it.
That is the machine. There is no magic. The quality comes from how well the model plans, how good the tools are, and how much of the right context it gathers.
Where web access changes the game
Here is a limit people hit fast: the model's knowledge is frozen at training time. The moment your task touches a library version newer than the cutoff, a niche error message, or an internal API doc, the agent is guessing.
Letting the agent read the live web fixes a big chunk of that. Two cases come up constantly:
Reading current docs. Libraries change. An agent that can fetch the current documentation page writes code against the real API instead of a remembered one.
curl https://link.sc/v1/fetch \
-H "Authorization: Bearer lsc_..." \
-d url="https://docs.example.com/api/v3/auth" \
-d format="markdown"
Clean markdown is exactly what a model wants: no navigation, no ads, just the content it needs to reason over.
Looking up an error. A cryptic stack trace often has a known cause documented in an issue or a discussion thread. Search returns full page content, so the agent gets the actual answer, not a snippet that trails off before the fix:
curl https://link.sc/v1/search \
-H "Authorization: Bearer lsc_..." \
-d query="ECONNRESET fetch undici node 22 keepalive"
If you are building this into your own agent rather than using an off-the-shelf tool, the link.sc docs show how to expose fetch and search as tools, and there is an MCP server at mcp.link.sc that plugs straight into editors and agents that speak the Model Context Protocol.
The honest limits
Agentic coding is genuinely useful and genuinely oversold. Both are true.
- It confidently does the wrong thing. An agent will happily implement a misreading of your request in a clean, well-formatted way. Vague instructions produce confidently wrong output.
- It struggles with large, tangled codebases. The more implicit knowledge a change requires, the more the agent flails. It cannot see the tribal knowledge that lives in your team's heads.
- It can churn. On a hard bug, an agent sometimes loops: change, test, revert, change again, without converging. You have to notice and step in.
- Review is not optional. Generated code needs the same scrutiny as a junior engineer's pull request, arguably more, because the agent has no stake in the outcome.
None of this makes the tools useless. It means you stay the engineer and the agent stays the fast, tireless, occasionally wrong assistant.
How to get real value from it
A few habits separate people who get a lot out of coding agents from people who get frustrated.
- Give tight, specific tasks. "Add pagination to the users endpoint, 20 per page, keep the existing response shape" beats "improve the API."
- Point it at the right context. Name the files, link the doc, paste the error. The less it has to guess, the better it does.
- Let it run the tests. An agent that can verify its own work catches its own mistakes. A test suite is the best guardrail you have.
- Give it web access. Current docs and error lookups turn guesses into facts.
- Review everything. Read the diff. Understand it. Do not merge code you cannot explain.
The takeaway
Agentic coding is a model plus tools plus a loop, aimed at your repo. It shines at well-scoped tasks where it can act and check its own work, and it stumbles on ambiguity and sprawling, implicit codebases. The single biggest upgrade you can give one is live web access, so it works from today's docs and real error solutions instead of a frozen memory. Keep reviewing the output, and it becomes one of the most useful tools in your workflow.
Building a coding agent that needs to read live docs and errors? Give it web access with link.sc in one API call.