Estimating Agent Tasks

Estimating Agent Tasks — ACR Framework

Overview

Agent Complexity Rating (ACR) replaces story points for AI-agent-assisted development. Story points measure human effort to BUILD. ACR measures the real bottleneck: how hard it is to VALIDATE, REVIEW, and INTEGRATE agent output.

When to Use

• Planning features, sprints, or roadmaps for AI-agent development
• Estimating time, cost, model, or tokens for a set of tasks
• Replacing story points in project planning documents
• NOT for: pure human development, non-coding tasks, single trivial changes

ACR = E + R + I (range 3-15)

Three axes, each scored 1-5:

E — Scope (Escopo)

| Score | Description | Example | |-------|-------------|---------| | 1 | 1 file, isolated change | Config tweak, typo fix | | 2 | 2-5 files, single feature | New component + route | | 3 | 6-15 files, cross-cutting | Endpoint + frontend + DB schema | | 4 | 15+ files, multi-domain | Auth migration across codebase | | 5 | Entire architecture | Full system redesign |

R — Review (Revisao)

R = pure human review time spent reading and evaluating agent output. Does NOT include time waiting for agent iterations (that's captured in I).

| Score | Description | Human time | |-------|-------------|------------| | 1 | Auto-verifiable (tests pass = done) | 1.5h |

I — Iteration (Iteracao)

Each cycle = one round-trip: human reviews output → gives feedback → agent adjusts. NOT agent-internal retries.

| Score | Description | Round-trips | |-------|-------------|-------------| | 1 | First-shot (agent nails it) | 1 | | 2 | Minor fixes | 2 | | 3 | Moderate back-and-forth | 3-4 | | 4 | Complex integration, many adjustments | 5-7 | | 5 | Exploratory, trial-and-error | 8+ |

ACR to Time Mapping

| ACR | Size | Wall clock (solo dev + agent) | |-----|------|-------------------------------| | 3-4 | XS | 30min - 1h | | 5-6 | S | 1-3h | | 7-9 | M | 3-8h (half day to full day) | | 10-12 | L | 1-2 days | | 13-15 | XL | 2-4 days |

Model Selection

| Condition | Model | Why | |-----------|-------|-----| | ACR >= 10 or security/auth critical | Opus | Deep reasoning, edge cases, architectural decisions | | ACR 7-9 (default) | Sonnet | Best cost-quality balance for standard implementation | | ACR Blended rates (3:1 input:output): Opus ~$30/M, Sonnet ~$6/M, Haiku ~$1.60/M. > On Max plan ($100-200/mo), use token estimates for quota planning instead of cost. > Range heuristic: Use lower bound for greenfield projects (less context to read). Use upper bound for legacy codebases (many files to read/understand before changing).

Parallelism

When tasks can run in parallel (subagents/teams), total time = critical path (longest sequential chain), not sum of all tasks.

How to calculate:

1. List all tasks with dependencies (use → for "blocks": Task A → Task B means B depends on A)
2. Draw the dependency graph
3. Find the longest path — that's your real timeline
4. Parallel tasks within that path add zero extra time

Example: Tasks A, B, C where A → C and B → C (C depends on both):

• Sequential time: A + B + C
• Parallel time: max(A, B) + C

Output Format

When estimating a project, produce a table like this:

| Task | E | R | I | ACR | Size | Model | Tokens ~ | Cost ~ | Time ~ | |------|---|---|---|-----|------|-------|----------|--------|--------| | Task name | X | X | X | XX | S/M/L | Opus/Sonnet/Haiku | XXK-XXM | $XX | Xh/Xd |

Then calculate:

• Total tokens (sum)
• Total cost (sum, considering model per task)
• Sequential time (sum of all times)
• Parallel time (critical path only)

Common Mistakes

| Mistake | Fix | |---------|-----| | Using story points habits (effort-based) | ACR measures review + iteration burden, not coding effort | | Same model for everything | Match model to task: Opus for hard, Haiku for trivial | | Summing time for parallel tasks | Use critical path, not total sum | | Ignoring iteration cycles | I axis is often the biggest time factor | | Overestimating scope of simple tasks | A 1-file change is E1 even if the file is complex | | Underestimating review for security tasks | Security/auth always gets R >= 4 |

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: sennaBruno
• Source: sennaBruno/claude-skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.