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Measure an AI coding agent from request telemetry, not by guessing from the visible transcript. Record input, output, cached-input, cache-write, context utilization, tool calls, retries, latency and outcome for every model request, including subagents. Then reduce the total cost of a successful task by shrinking irrelevant context, preserving cacheable prompt prefixes, compacting or restarting sessions at sensible boundaries, routing bounded work to lighter models, and eliminating redundant tool loops.
A high cache-hit percentage or a short individual prompt is not proof of savings. Tool results, retries, subagent calls and human rework can outweigh cached-input benefits. The useful target is lower cost and latency per accepted patch while keeping test success and quality stable.
What “token usage” means in an agent workflow
An agent run is a tree of model requests rather than one prompt and one answer. The main agent may call a model repeatedly, invoke tools, feed their results back into context, and delegate subtasks to one or more subagents. Every request can have a different input and output size. Treating the transcript as a single token count hides the causes of cost.
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Keep the token dimensions separate
| Dimension | What it measures | Why it matters |
|---|---|---|
| Input tokens | Tokens sent to the model for a request, including instructions, history, tool definitions and tool results. | Usually grows as the session accumulates context. |
| Output tokens | Tokens generated by the model. | Long plans, explanations and patches can dominate a request even when input is stable. |
| Cached-input tokens | Input tokens served from a provider’s prompt cache. | Often cheaper or faster, but it is only one component of task cost. |
| Cache-write tokens | Tokens written into a cache when a reusable prefix is created or refreshed. | Cache creation can add work; count it rather than assuming every cache hit is free. |
| Context-window utilization | The portion of the model’s available context occupied by a request. | High utilization increases compaction pressure and can force a reset or truncation. |
| Tool calls | Shell, search, editor, test, browser or integration operations requested by the agent. | Each call can create another model turn and add a large result to the next input. |
| Retries | Repeated requests caused by errors, timeouts, invalid tool arguments or failed tests. | Retries can multiply total usage while producing no additional accepted work. |
| Subagent usage | Tokens consumed by delegated agents and their tool calls. | Subtasks belong in the total-task cost, even when the main transcript looks small. |
Track these fields independently. A run with many cached input tokens can still cost more than a run with fewer cache hits if it produces uncached tool output, retries several times, or launches multiple subagents.
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Build telemetry that can answer “how many tokens did this task use?”
Log every model request
Create one structured record per request. At minimum, include:
- timestamp, model and provider;
- task ID, session ID, parent request ID, agent ID and subagent ID;
- input, output, cached-input and cache-write token counts when the provider exposes them;
- context-window utilization or the raw context size;
- tool-call count and tool names;
- retry number, latency, error status and final outcome; and
- repository revision, task category and whether the resulting patch passed its acceptance tests.
Keep request records immutable and aggregate them by task ID. A task total should include every descendant request in the agent tree. Store the raw provider response as well as normalized fields so a later SDK change does not destroy your audit trail.
Use provider telemetry instead of transcript estimates
Anthropic’s token-counting endpoint provides a preflight estimate of input tokens before a message is sent. It is useful for cost, rate-limit, model-routing and context-window decisions, especially when a prompt contains many files or generated command results. A preflight estimate is not a bill: record the actual usage returned after completion as the authoritative value.
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For other providers, use their response usage fields or middleware hooks. Do not infer usage by counting characters in a transcript: tokenizers differ, tool messages may be hidden from the UI, and cached or retry requests can be omitted from a displayed conversation.
Attribute the whole work tree
Represent delegation as a parent-child graph. The main agent’s total is not the task total if it delegates a search, test diagnosis or implementation to another agent. OpenAI’s observability guidance recommends inspecting main-agent and subagent usage and warns that cache-hit percentage alone does not measure savings on the complete task. Sum all descendants, then retain the per-agent breakdown so an unexpectedly expensive branch can be fixed without obscuring the total.
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A repeatable measurement procedure
- Define the unit of work. Choose a task boundary such as “issue opened to accepted patch.” Give every run a task ID and record the repository revision, model configuration, instructions and acceptance tests.
- Instrument before optimizing. Add request-level logging for the fields above. Include failed requests and retries; excluding them makes an unreliable workflow look efficient.
- Count large prompts before sending them. Run a provider’s token-counting endpoint or equivalent preflight counter on prompts assembled from many files, generated logs or large histories. Use the estimate to reject or reshape an oversized request before it consumes a context window.
- Assemble a representative baseline. Select tasks that reflect your normal mix: localized edits, bug fixes, test generation, refactors and multi-file changes. Keep repository state, model, system instructions, tool set and acceptance tests fixed while collecting the baseline.
- Capture outcomes, not just usage. Mark whether tests passed, a reviewer accepted the patch, a retry was needed and how much human rework followed. A low-token failed patch is not an optimization.
- Aggregate by task and by agent. Sum input, output, cached-input and cache-write tokens across all requests and descendants. Also total tool calls, retries and wall-clock latency.
- Report distributions. Show median, upper percentiles and outliers for each task type. A grand average can conceal a few retry-heavy runs that dominate spend.
A compact event record can be represented as JSON for your own logging pipeline:
{"task_id":"…","session_id":"…","agent_id":"main","parent_request_id":null,"model":"…","input_tokens":0,"output_tokens":0,"cached_input_tokens":0,"cache_write_tokens":0,"context_utilization":0,"tool_calls":0,"retry":0,"latency_ms":0,"outcome":"passed"}The ellipses above are field values to be supplied by your logger, not values to estimate from the transcript. Keep the schema stable while provider adapters translate their native usage fields.
Diagnose why a coding task became expensive
Context grew beyond the task’s needs
Every command result, file excerpt and earlier discussion that remains in the conversation can be retransmitted on later turns. A small change request can therefore become a large series of inputs. Repeatedly attaching the same unchanged files has the same effect.
Tool output was verbose or unbounded
Full test logs, recursive directory listings, generated bundles and stack traces can consume more input than the original issue. The agent may then spend additional turns searching inside its own output. Prefer bounded commands, structured output and summaries that preserve the lines needed for a decision.
Retries and recovery loops multiplied calls
Invalid tool arguments, flaky tests, authentication failures and timeouts all create extra requests. A model that rereads the same file after each failure can consume many tokens without changing the patch. Log retry causes separately so you can distinguish a prompt problem from an infrastructure problem.
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Delegation added overhead
Subagents are useful when work can proceed independently, but each receives instructions, context and tool results. Delegating a tiny edit can cost more than performing it in the main session. Compare the subtask’s success rate and total tokens with the un-delegated baseline.
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The cacheable prefix changed
Prompt caching generally reuses a rendered context only when its prefix matches. Editing a system instruction, changing tool definitions or inserting volatile text near the beginning can invalidate reuse for all later requests. Cache writes and uncached input then rise together.
Reduce usage by controlling context
Scope the task and repository orientation
Give the agent one bounded objective, the files or symbols likely to matter and explicit acceptance tests. Maintain a concise repository map in an instruction file such as AGENTS.md (or your platform’s equivalent): architecture, build commands, test conventions, generated-file rules and directories that should not be edited. A short map prevents repeated exploratory reads without copying the whole repository into every prompt.
- State the requested behavior and non-goals.
- Name relevant packages, entry points and tests when known.
- Ask for focused searches instead of a full-tree dump.
- Return only the lines or records needed to decide the next step.
- Exclude secrets, generated artifacts and unrelated integrations.
Trim command output at the source
Use commands that select a range, filter by severity or emit machine-readable fields. Cap log lines and request a summary plus the exact failing locations. If a tool returns a large result, store it outside the conversation and send a short digest with a way to retrieve a specific slice. Do not repeatedly send an unchanged result.
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Configure tools per task
A formatting task does not need every deployment, database and browser schema. Expose only the tools required for the current phase. Fewer schemas reduce the stable prompt size and reduce opportunities for irrelevant calls.
Preserve prompt-cache reuse
Put stable material first: system instructions, tool definitions, security rules and repository guidance that remain constant across requests. Put volatile material later: the issue text, current file excerpts, test output and newly discovered facts. This arrangement gives requests in the same workflow a matching prefix for as long as possible.
Do not insert timestamps, random IDs, changing status banners or per-request tool output into the stable portion. When a rule or tool schema genuinely changes, expect a new cache write; do not distort instructions merely to preserve a stale prefix.
Measure cache-write tokens and uncached input alongside cached-input tokens. A high cached-input share can coexist with high total cost when the uncached suffix is large or when many tool turns and retries follow. Evaluate caching by total successful-task cost, latency and quality, not by the cache percentage in isolation.
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Compact or restart sessions deliberately
Compact a long, coherent phase
Set a compaction threshold before the context window is crowded. Compaction reduces context size while preserving the state needed to continue a long-running interaction. Before compacting, ask the agent to retain decisions, changed files, unresolved failures, test commands and constraints; discard exploratory dead ends and duplicated output. OpenAI describes compaction as a way to balance quality, cost and latency as conversations grow.
Start a new session between unrelated phases
Reset after investigation when implementation begins, after implementation when review begins, or whenever the old history no longer helps the next objective. Carry forward a concise handoff: current revision, files changed, decisions, failing tests and the next acceptance condition. A fresh session avoids paying for stale exploration on every subsequent request and creates a clear measurement boundary.
Use a practical reset signal
- Context utilization approaches the configured threshold.
- The agent starts rereading files or repeating earlier conclusions.
- Tool results are being truncated or summarized so heavily that decisions become unreliable.
- The task changes from diagnosis to an unrelated feature or maintenance request.
- Retries increase after a long history, suggesting that relevant instructions are being diluted.
Route work by difficulty, not habit
Use a lighter model for bounded formatting, localized edits, straightforward test generation or mechanical migrations when its acceptance rate is adequate. Reserve larger reasoning budgets for ambiguous debugging, cross-cutting refactors and tasks where a wrong patch creates expensive rework. Route by measured outcomes rather than a model’s advertised price.
For each route, compare total tokens per successful task, latency, test and review success, retry rate and human rework. A cheaper request that needs two corrective runs can cost more than one larger request that succeeds immediately. Keep the same task set and acceptance criteria when comparing routes.
Remove redundant loops and tool calls
- Cap retries by failure class; stop automatically on authentication or permission errors that a model cannot repair.
- Detect unchanged file reads and avoid sending the same contents again.
- Use focused search commands before opening large files.
- Return structured tool errors with the actionable field, not an entire trace when it is unnecessary.
- Ask for a plan only when planning will prevent costly implementation mistakes; otherwise let a bounded task proceed directly.
- Stop after acceptance tests pass instead of requesting additional speculative cleanup.
Instrument each loop so you can verify that a change lowers total successful-task cost rather than merely shortening one request.
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| Axis | Question to answer | Interpretation |
|---|---|---|
| Total tokens per successful task | What did the complete main-agent and subagent tree consume? | Primary efficiency measure. |
| Input versus output share | Is cost driven by context or generation? | Context trimming and output limits address different problems. |
| Cached versus uncached input | How much of the prefix was reused? | Useful diagnostic, not a standalone savings claim. |
| Cache-write overhead | How often did prefixes need to be created or refreshed? | Frequent writes may indicate unstable prefixes or short sessions. |
| Context utilization | How close were requests to the window limit? | High values predict compaction, truncation or resets. |
| Tool calls and retries | How many extra turns were generated? | Often the fastest place to remove waste. |
| Latency | How long until an accepted patch? | Token reduction that slows delivery may not be beneficial. |
| Patch and test success | Did the first result meet acceptance criteria? | Protects quality while optimizing cost. |
| Human rework | How much manual correction followed? | Include this when judging total task value. |
Report medians and distributions by task type, model route and session policy. Keep one baseline configuration and change one variable at a time—context scope, prefix layout, compaction threshold, tool set or routing—so an improvement has an identifiable cause.
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Provider-specific checkpoints
Claude-based coding workflows
Use Anthropic’s token-counting endpoint to preflight unusually large assembled prompts, then compare the estimate with actual usage in the completion response. This is particularly useful before sending a repository snapshot, a long test log or a multi-file patch. The endpoint counts input; your task ledger must still add output, cache fields, tools, retries and subagents.
GitHub Copilot SDK workflows
Read the SDK’s per-call token counts, context-window utilization and aggregate credit cost, and attach them to your own task IDs. Keep failed calls and delegated work in the same ledger. SDK field names and billing semantics are version-sensitive, so record the SDK version and retain raw responses.
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Inspect usage for the main agent and every subagent. Use compaction when a coherent conversation grows large, and evaluate cache reuse against total task cost rather than treating a high cached-input percentage as proof of savings.
A cost-and-quality decision rule
For a task, calculate total provider cost from the provider’s current pricing and recorded token categories, then add operational indicators: latency, retry burden and estimated human rework. Compare that result with the baseline for the same task class. Do not publish a universal “tokens saved” percentage: pricing, tokenizer behavior, cache duration and SDK fields vary by provider, model, edition and date.
An optimization is successful when the accepted patch arrives with equal or better test and review quality, no unacceptable security or reliability regression, and lower total cost or latency. If token usage falls but failures or manual fixes rise, revert the change and investigate the new failure mode.
Quick Recap
Operational checklist
- Every request has task, session, agent and parent identifiers.
- Input, output, cached-input and cache-write tokens are stored separately.
- Tool calls, retries, latency, context utilization and outcome are recorded.
- Subagent requests roll up into the parent task total.
- Large prompts are preflight-counted before sending.
- Stable instructions and tool schemas precede volatile task data.
- Command output is bounded, focused and not resent unchanged.
- Compaction and session-reset thresholds are explicit.
- Model routing is judged by successful-task cost, not call price alone.
- Baselines use representative tasks and report medians plus outliers.
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