Keeping the signal clean during conversational coding sessions
During extended coding sessions with an LLM, a subtle problem emerges: context pollution. Every test run, every git commit, every linter invocation adds to the conversation history. If a test suite outputs 200 lines of stack traces, that noise becomes part of the context the model carries forward. The conversation gradually fills with administrative debris that has nothing to do with the domain problem being solved.
When Claude Code runs, it maintains a conversation transcript—a running record of every message, tool invocation, and output. This transcript is what gives the model “memory” across a session. When asking about a function defined earlier, the model can reference it because that definition exists somewhere in the transcript.
But this memory is finite. There’s a context window, and everything competes for space within it. A hundred-line test output consumes the same resources as a hundred lines of carefully crafted domain logic. Worse, the model weighs recent context heavily. If the last thing in the conversation is a wall of test failures, that noise colors the model’s next response—even when the task at hand has nothing to do with testing.
The result: after enough administrative churn, the session loses coherence. The model starts forgetting earlier decisions. Responses become less precise. The signal-to-noise ratio degrades.
One solution to this problem is the agent—a subprocess that runs in its own isolated context, separate from the main session.
When an agent is spawned, it receives a focused prompt describing a specific task. It executes that task, potentially invoking tools, reading files, running commands. But all of that activity happens in the agent’s own transcript. When the agent completes, only its final summary returns to the parent session.
If a test suite produces 500 lines of output, the agent sees all of it—but the parent session only sees something like “Tests passed: 47/47” or “3 failures in authentication module.” The noise stays contained. The parent context remains clean.
This isolation works because the agent’s full transcript never merges back into the main conversation. The parent receives a distilled result, not a dump of everything that happened.
Agents become most valuable for tasks that are:
If a test suite runs ten times during a debugging session, and each run produces 100 lines of output, that’s 1000 lines of context consumed by testing alone. With an agent handling test runs, the parent session might only accumulate 10 lines of summary output—a 99% reduction in administrative noise.
Similarly for git commits: the diff, the commit message composition, the status checks—all of that can happen inside an agent. The parent session just learns “committed changes to authentication module” and moves on.
Claude Code provides a built-in wizard for creating custom agents. Running /agents opens the configuration interface.
The wizard prompts for:
Once configured, agents can be invoked manually or triggered automatically based on patterns Claude Code recognizes in the conversation.
For a test-running agent, the prompt template might be:
Run the test suite and report results. If tests fail, provide a brief summary of which tests failed and why. Do not include full stack traces unless specifically relevant to diagnosing the failure.
This instructs the agent to filter its own output before returning—applying compression at the source.
Agents aren’t free. Spawning an agent has overhead: the subprocess needs to load context, execute, and return. For quick operations, this overhead might exceed the context savings. If running a single fast command that produces one line of output, an agent adds latency without meaningful benefit.
The calculation tips toward agents when:
For long sessions focused on complex problems, context preservation often matters more than shaving seconds off individual operations. The agent’s isolation pays dividends in sustained session quality.
Context management isn’t glamorous. It doesn’t produce visible artifacts or impressive demos. But for practitioners spending hours in conversational coding sessions, it’s the difference between a session that stays sharp and one that gradually loses the thread. Agents are one tool for keeping the signal clean—letting the conversation stay focused on the work that actually matters.