Terminal AI Coding Agents: A Practical Usage Guide from Beginner to Advanced

Over the past two years, a class of AI coding agents that “run in the terminal, read and write project files directly, and execute commands” has quickly become part of everyday engineering work. These tools no longer just complete a single line of code — they can take on a full chain: read the codebase, locate the problem, change multiple files, run the tests, and report a conclusion. Yet many developers describe the same experience: the demos are impressive, but day-to-day usage keeps getting stuck — wrong files edited, requirements misread, changes getting messier with every iteration, and a single change spanning hundreds of lines that nobody can review.

The problem is usually not the tool itself but how it is used. Setting specific products aside, this article treats these tools as a general technical category and lays out a reusable usage methodology.

1. Understand the Capability Boundaries Before Talking About Speed

Unrealistic expectations are where every “stuck” moment begins. A coding agent built on a large language model is fundamentally doing high-intensity pattern matching and reasoning, and its strengths and weaknesses are both fairly clear.

What it does well:

• Targeted search and structural mapping in an unfamiliar codebase (“what is the call chain for this endpoint?”);
• Pattern-based, repetitive, cross-file consistent changes (renames, bulk type annotations, unifying log formats);
• Writing tests, constructing boundary cases, and doing a first pass on locating an error from a stack trace;
• Generating scaffolding, configuration files, scripts, and documentation drafts;
• Turning a vague description into an actionable draft technical plan.

What it does poorly, or where the risk is high:

• “Refactoring” legacy modules that lack test coverage and type constraints;
• Design decisions that involve business rules, compliance requirements, or implicit domain knowledge;
• Direct changes to production, data migration scripts, and irreversible operations;
• Globally consistent reasoning over very long contexts — the later it gets, the more easily it “forgets” constraints stated earlier.

The conclusion: treat it as a fast-handed senior executor that requires strict acceptance criteria, not as a technical lead who can make decisions for you.

2. Environment Setup: Three Things That Determine Everything Afterward

2.1 The startup directory is its field of view

Most terminal-based tools collect context with the current working directory as the root. Starting from the repository root lets the agent see build scripts, configuration, documentation, and source code at the same time; starting from a subdirectory often means it misses key conventions and produces changes that are “technically correct but do not match project standards.”

# Always start from the repository root so project structure and rule files are visible
cd /path/to/your/repo
git status            # Confirm a clean working tree so each change can be reviewed individually
git switch -c feat/xxx

2.2 Get credential injection right from the start

Credentials should be injected via environment variables or a system secrets manager, and never written into repository files, comments, or commits. Also give the agent’s execution environment its own cloud or local account, follow the principle of least privilege, and grant access only to the repositories and resources it needs.

2.3 Create a project rules file

This is the highest-return step. Most tools read a rules file from the repository root by convention (check the tool’s own documentation for the exact filename and loading priority). It removes the waste of re-explaining project background every single time.

Example content (trim it to your own project):

# Project Rules (example)
## Stack and directory conventions
- Backend: Java 17 + Spring Boot, layered as controller / service / repository
- Frontend: TypeScript + React, components in src/components, no direct calls to the request layer from components
## Build and verification commands
- Unit tests: mvn -q test
- Static checks: mvn -q spotless:check
## Hard constraints
- Do not modify existing database migration files; additions only
- Any new third-party dependency must state its purpose, maintenance status, and license
- No plaintext secrets, internal hostnames, or real user data in the code
## Delivery requirements
- Every change must come with unit tests, and the reply must list the commands actually run and their results
- Keep a single change under 200 lines where possible; if it will exceed that, present the plan first

Note: a rules file is not better for being longer. It may be loaded into context on every run, and redundant content crowds out the attention budget and actually lowers instruction adherence.

3. The Five-Step Standard Workflow: Pushing Uncertainty to the Minimum

Step 1: Clarify the goal

Before writing any instruction, answer three things yourself: what is the input, what is the expected output, and what are the acceptance criteria. Miss any one of them and you are very likely to end up with “a pile of changes that did not solve the problem.”

Step 2: Collect context in a targeted way

Do not let it “read the whole project first.” Give it precise entry points; usually two or three rounds of targeted retrieval are enough:

First read src/order/OrderService.java, src/order/OrderRepository.java,
and docs/order-state.md, then answer only this:
What are the legal paths for order state transitions? Which method performs the validation?
Do not modify any files.

Read-only first, write later — this is the single most effective way to control risk.

Step 3: Plan before acting

For cross-file or design-level changes, require a plan first and explicitly forbid touching code:

Based on the code above, give me a change plan that includes:
1) The list of affected files and the intent of each change;
2) Compatibility impact on the database or APIs;
3) The rollback approach;
4) The list of test cases that need to be added.
Do not modify any files in this round. Wait for my confirmation before executing.

Step 4: Execute and verify in small steps

Once the plan is approved, advance one verifiable unit at a time rather than changing everything at once. At each step, require it to run the tests and static checks itself and paste back both the commands and the real output. The key point: verification evidence must come from actual execution, not from inference.

Step 5: Review and consolidate

When the task wraps up, have it output a change summary: which files changed, why, what is left outstanding, and what to do next. Fold any recurring conventions back into the rules file, and write the pitfalls you hit into a team checklist. Most of the improvement in results comes from accumulating this step over time.

4. Writing Instructions: The Four-Part Structure

High-quality instructions have a very stable shape: goal, constraints, reference, acceptance.

[Goal] Add idempotency protection to OrderService.cancel() so that repeated calls return the first result.
[Constraints]
- Only change code inside the order package; do not touch shared utility classes;
- Do not add dependencies or change the existing database schema;
- Use the existing requestId field as the idempotency key.
[Reference] Follow the existing idempotency implementation in src/pay/PayService.java.
[Acceptance]
- Add unit tests covering: first cancellation succeeds, repeated cancellation returns the same result, and only one of 10 concurrent threads takes effect;
- Run mvn -q test -Dtest=OrderServiceTest and paste the output;
- Finish by listing the changed files and the line-count delta.

Counter-examples are instructive too. Saying only “help me optimize the order module and mind the code quality” will almost certainly produce a large volume of meaningless changes.

5. Context Management: Controlling the Attention Budget

A common cause of failure on long tasks is not insufficient capability but key information being drowned out.

• Narrow first, then widen: start from two or three files, confirm its understanding is correct, then expand the scope.
• Give summaries, not full text: for large files, have it extract the structure or key fragments first, then discuss based on that summary.
• One topic per task: do not switch between business modules within the same session — switching is expensive and easily causes cross-contamination.
• Persist the plan: write the plan and progress into a planning document in the repository, so a long task that gets interrupted can be resumed from that document without restating the full background.
• Explicit exclusions: tell it which directories not to scan (build artifacts, generated code, large binary assets). This saves time and avoids noise.

6. Permissions and Risk Tiers

Grant permissions progressively across four tiers; the later ones need human confirmation each time.

Tier	Operation type	Recommended policy
L0	Read files, search, explain	Auto-approve freely — this is where most of the value comes from
L1	Write files in the workspace	Allow, but only on a dedicated branch
L2	Run builds, tests, static checks	Allow, restricted to local or an isolated environment
L3	Install dependencies, run scripts, access the network, touch production	Confirm each item manually; no automatic execution

Supporting security baseline:

• Execute inside a container or a separate workspace, and limit what the agent can reach on the host;
• Keep production credentials, customer data, and internal secrets out of the context; use masked data when sample data is needed;
• For irreversible operations such as data migrations, deletions, and bulk updates, have the agent produce the script, review it manually, and run it only after a backup;
• At the end of each session, check git diff to confirm no unexpected files, debug code, or secrets were introduced.

7. Closing the Loop with Your Existing Engineering System

Point usage yields limited returns. Real efficiency comes from embedding the agent into existing processes:

• Version control: one commit per task, with a message stating the intent and the verification method, keeping review cost manageable;
• Code review: a human always stays in the loop. During review, focus on where these tools tend to go wrong — boundary conditions, exception branches, concurrency assumptions, and whether tests were “adjusted to pass”;
• Testing and CI: hard-wire unit tests, static checks, and coverage gates into the pipeline so a machine catches problems first;
• Observability: after a change ships, watch error rates, latency, and alert changes to confirm the expected benefit actually materialized.

8. Team Adoption and Measurement

Beyond individual technique, a team needs three pieces of infrastructure: a rules template library (standard rules files maintained per stack), knowledge base retrieval integration (architecture documents, API contracts, historical decision records), and a shared set of metrics.

Metrics worth tracking (collect your own data; do not copy outside numbers): first-pass task success rate, the share of human-edited lines relative to agent output, defect density at the review stage, average delivery cycle time per requirement, and the number of security issues caught. Use data to decide whether a given class of task is genuinely suited to an agent, rather than going on intuition.

On the cloud side, these capabilities are typically delivered through general-purpose product categories: object storage for team knowledge documents and rule templates, a container platform for isolated execution environments, log services plus monitoring and alerting to observe invocation volume and failure rates, and a message queue to distribute batch refactoring tasks asynchronously. Cloud providers such as Baidu AI Cloud offer corresponding products in these general categories; choose based on your own compliance and cost requirements.

9. Common Failure Troubleshooting Checklist

Symptom	Likely cause	Fix
Unrelated files get changed	Vague goal, unbounded scope	Add “only files in package X may be modified”
Repeatedly fails at the same spot	No executable verification, or the test itself is wrong	Require a failing test first, then the implementation
Ignores earlier constraints late in the task	Context too long, constraints not persisted	Write hard constraints into the rules file
Generated dependency does not exist	Invented a library or version	Require “confirm existence and the latest stable version before adding a dependency”
All tests green but behavior is wrong	Weak assertions, or tests were loosened	Manually verify assertion semantics; forbid changing existing assertions
Answers are verbose and miss the point	No output format specified	Require “conclusion first, no more than N lines”

10. A Four-Week Onboarding Path

• Week 1: use it only in read-only scenarios (reading code, explaining logic, drafting tests) to build an intuition for output quality, and finish a first draft of the rules file.
• Week 2: take on single-file, small-scope changes on a dedicated branch, strictly following the “plan first, then self-verify” flow.
• Week 3: try cross-file refactoring and defect localization, and introduce an isolated execution environment plus pipeline gates.
• Week 4: turn rules templates, instruction templates, and the review checklist into team assets, and evaluate the payoff with metrics — including identifying which task types are not suited to delegation.

The core judgment comes down to one line: wrap an unpredictable generation process inside a reliable acceptance system. Once verification is dependable enough, the output of these tools shifts from unpredictable drafts to changes you can merge with confidence.