Claude Code Agentic Workflows

Claude Code Agentic Workflows: Multi-Step Execution Without Constant Prompting

Most developers first experience Claude Code as a faster way to write code. They describe what they want, Claude Code writes it, they review. One prompt, one output, move on. For a structured introduction to Claude Code fundamentals before diving into agentic patterns, the Claude Code course covers the mental models and prompting discipline that make agentic execution reliable.

Agentic workflows are a different pattern entirely. Instead of producing a single output for each prompt, Claude Code plans a sequence of actions, executes them, observes the results of each step, and iterates until the task is complete. A prompt that used to require ten separate interactions can complete as a single agentic run.

The distinction matters because it changes what kinds of tasks are practical to delegate to Claude Code. Tasks that require fifteen steps to complete are only worth delegating if you do not have to supervise every step.

An agentic workflow is not a better autocomplete. It is a different model of delegation: describe the outcome, define the acceptance criteria, let Claude Code navigate the path.

The Agentic Loop

Every Claude Code agentic workflow runs the same underlying loop:

Think: Claude Code reads the task, the relevant files, and any test output or error messages. It builds a model of the current state and the required end state.

Act: Claude Code takes a specific action: writes code, modifies a file, runs a command, reads a new file.

Observe: Claude Code reads the result of that action. Did the test pass? Did the build succeed? What does the error output say?

Iterate: Based on what it observed, Claude Code decides the next action. If the test passed, move to the next step. If it failed, diagnose and fix before continuing.

This loop runs continuously until the task is complete or Claude Code reaches a state it cannot resolve without human input. The number of loop iterations depends on the complexity of the task. A simple feature might complete in 8 iterations. A complex refactor with test failures might run 40.

The loop is what makes agentic workflows powerful: Claude Code does not need a new human prompt at every step. It observes and adapts autonomously.

Five Agentic Workflow Examples

1. Build and Test a New Feature

Prompt: "Build the user notification preferences endpoint. Spec is in docs/notification-spec.md. All tests must pass before committing."

Steps (approximately 12):

Read the spec document
Read existing user model and related endpoints
Write the new endpoint code
Write unit tests for the endpoint
Run the test suite
Read test failure output
Fix the identified failure
Run tests again
Confirm all tests pass
Check code style against existing patterns
Fix style inconsistencies
Commit with a descriptive message

Claude Code does not stop after writing the endpoint and ask “should I run tests?” It runs them as part of the defined task. The acceptance criteria, “all tests must pass before committing,” is embedded in the task description.

2. Refactor a Module

Prompt: "Refactor the payment processing module to replace the direct Stripe API calls with the new PaymentService abstraction. All existing tests must still pass."

Steps (approximately 18):

Read the current payment processing module
Read the PaymentService abstraction and its interface
Identify all direct Stripe API call sites
Plan the replacement sequence 5-16. Replace each call site, running relevant tests after each substitution
Run the full test suite
Commit if all tests pass, or fix remaining failures and rerun

The module-level refactor is precisely the kind of task where agentic execution saves significant time. Replacing ten call sites one by one, running tests between each, and handling the failures that emerge is tedious work. Agentic Claude Code handles the loop while you work on something else.

3. Generate a Test Suite

Prompt: "Write unit tests for the OrderService class. Target 85% coverage. Tests should follow the existing Jest patterns in the test directory."

Steps (approximately 14):

Read the OrderService implementation
Read existing tests for pattern reference
Identify untested methods and edge cases
Write an initial test file
Run the test suite with coverage reporting
Read coverage output to identify gaps
Write additional tests for uncovered paths
Run coverage again
Repeat until 85% coverage is reached 10-13. Fix any test failures introduced by the new tests
Commit the test file

Coverage targets are natural acceptance criteria for agentic test generation. Claude Code runs the coverage tool, reads the report, and writes more tests for the gaps.

4. Debug Failing CI

Prompt: "The CI pipeline is failing on the integration tests. Read the latest failure logs from ci-logs/latest.txt and fix the root cause."

Steps (approximately 10):

Read the CI log file
Identify the failing test and error message
Read the test file that is failing
Read the code the test is exercising
Identify the likely cause of failure
Implement the fix
Run the relevant tests locally
Confirm the fix resolves the failure
Check for related tests that might be affected
Commit the fix with an explanation of the root cause

CI debugging is well-suited to agentic execution because the workflow is structured: read logs, identify cause, fix, verify. The loop terminates with a clear success condition.

5. Migrate a Database Schema

Prompt: "Write a migration to add a \status` column to the orders table. Default value is ‘pending’. Update all queries that touch the orders table to include the new column. Run the test suite to confirm nothing breaks.”`

Steps (approximately 16):

Read the current schema file
Identify the migration framework in use
Write the migration file
Read all files that query the orders table 5-12. Update each query to include the status column
Run the test suite
Fix any test failures caused by the schema change
Run the full suite again to confirm
Commit migration and code changes together

Schema migrations are a high-stakes case for agentic workflows. The task is well-defined, the acceptance criteria is clear (tests pass), and the work is tedious enough to benefit from automation. Use plan mode before executing a schema migration agentic workflow.

How to Structure Tasks for Agentic Execution

Agentic workflows succeed when the task has three properties:

A clear start state. “The tests are currently failing” or “the feature does not exist yet” gives Claude Code an unambiguous baseline. Vague start states produce vague plans.

A clear end state. “All tests pass and the feature is committed” is a testable end state. “Make the code better” is not. The end state should be something Claude Code can verify without asking you.

Testable acceptance criteria. Tests, coverage thresholds, lint checks, and build success are all machine-verifiable. They let Claude Code determine autonomously whether the task is complete. Tasks with purely subjective acceptance criteria, “make it cleaner”, require human review at every iteration and break the agentic loop.

The practical prompt pattern:

Task: [what to build or change]
Context: [relevant files or specs]
Acceptance criteria: [verifiable conditions for completion]
Constraints: [anything Claude Code should not touch]

Tasks structured this way complete reliably. Tasks that omit the acceptance criteria tend to produce output that is plausible but unverified.

Where Agentic Workflows Break Down

Unclear acceptance criteria. If the task has no machine-verifiable end condition, Claude Code cannot determine when it is done. It will either stop early or keep iterating without converging. Add a test requirement or a specific output specification.

Missing tests. Agentic workflows that cannot run tests to verify progress are blind. Claude Code can write code that compiles but behaves incorrectly, with no signal to trigger another iteration. If a codebase has no test infrastructure, build minimal test coverage before running complex agentic workflows.

Ambiguous scope. A prompt that could reasonably be interpreted in two different ways will be interpreted in one of them, and it may not be the one you intended. Ambiguity that a human would ask about gets resolved silently in agentic execution. Be specific about which module, which function, which files.

Circular failures. Sometimes Claude Code hits a failure it cannot resolve: a test that fails due to a flaky external dependency, an environment issue that requires a configuration change it cannot make, or a bug that requires context it does not have. It will iterate, but will not converge. Set a --max-turns limit and review the output when it stops rather than letting it run indefinitely.

The best agentic workflow prompts read like a good ticket: clear description, clear acceptance criteria, clear scope boundaries. If you would not close a ticket with that description on a human engineer, do not send it to an agentic Claude Code session.

Frequently Asked Questions

How is an agentic workflow different from just having a long conversation with Claude Code?

In a standard conversation, Claude Code produces output and waits for your next prompt at every step. In an agentic workflow, Claude Code takes actions, observes results, and decides the next step autonomously. The human is not in the loop between iterations. This is what makes agentic workflows practical for tasks with many steps: the human overhead is front-loaded into the task description, not distributed across every step.

Can agentic workflows run tests automatically?

Yes. If your task description specifies that tests should pass before completion, Claude Code will run the test suite, read the output, fix failures, and rerun. The test runner needs to be accessible from the terminal in Claude Code’s working environment. Include the test command in your task description or CLAUDE.md so Claude Code knows how to invoke it.

What happens when an agentic workflow gets stuck?

Claude Code will continue iterating until it either completes the task or exhausts its turn limit. If it hits a failure it cannot resolve, it will try alternative approaches and eventually report the blocker. Use the --max-turns flag to limit autonomous execution and review what happened when it stops. The output will show exactly where it got stuck and what it tried.

How do I know if a task is suitable for agentic execution?

Ask three questions: Can I write a clear acceptance criteria that Claude Code can verify? Is the task bounded enough that I can predict roughly which files it will touch? Would a competent developer with the same information be able to complete this task without asking clarifying questions? If the answers are yes, it is a good agentic candidate.

Want to run agentic workflows on your team’s codebase?

The patterns here work best when your tasks have clear acceptance criteria and your test infrastructure is in place. Start with one well-scoped agentic task, verify it completes reliably, then expand to more complex workflows.

Path one: start running agentic workflows yourself. The prompting structure in this article is the foundation, use the task/context/acceptance-criteria/constraints format and add --max-turns to maintain control during early sessions. The Claude Code course covers the session and prompting fundamentals that make agentic execution reliable.

Path two: work with Phos AI Labs. If you want agentic workflows designed and implemented for your engineering team’s specific stack and development process, Phos AI Labs is a CCA-F certified Claude implementation partner that helps organizations identify where autonomous execution adds value and where human checkpoints are required. Thirty minutes, no deck. Start here.