Peer Benchmark Report
See how your AI agent project compares to 200+ real engagements — budget ranges, team composition, timelines, and framework adoption across industries.
Based on aggregated data from scope estimator submissions + industry research. Updated quarterly. All figures are medians and percentiles across real project engagements — not vendor-supplied estimates.
Budget by Project Type
P25 / Median / P75 budget ranges and median timeline from 200+ engagements. AI automation agency costs vary significantly by project scope and complexity.
| Project Type | P25 Budget | Median Budget | P75 Budget | Median Timeline |
|---|---|---|---|---|
| Customer Support Bot | $8k | $22k | $55k | 6 wks |
| Sales Automation Agent | $15k | $40k | $90k | 10 wks |
| Internal Process Automation | $12k | $35k | $75k | 8 wks |
| Data Pipeline Agent | $20k | $60k | $130k | 14 wks |
| Multi-Agent System | $40k | $110k | $250k | 20 wks |
P25 = 25th percentile (budget-conscious engagements). P75 = 75th percentile (larger scope or enterprise requirements).
Team Composition Benchmarks
Typical team structures for hiring AI agent developers, segmented by project budget. Larger budgets demand broader skill coverage and dedicated QA.
Tight scope, single-agent, well-defined integration surface.
- ›1 × Project Manager
- ›1 × Solutions Architect
- ›1 × AI Developer
Multi-tool workflows, moderate integrations, production hardening.
- ›1 × Project Manager
- ›1 × Solutions Architect
- ›2–3 × AI Developers
- ›1 × QA Engineer
Multi-agent platforms, compliance requirements, enterprise SLAs.
- ›1 × Project Manager
- ›2 × Solutions Architects
- ›4–6 × AI Developers
- ›2 × QA Engineers
- ›1 × DevOps Engineer
Timeline Factors
These are the most common factors that extend or compress AI agent project delivery timelines, based on post-engagement retrospectives.
- +Compliance requirements (HIPAA, SOC 2, GDPR)
- +Poorly documented or unreliable data sources
- +Legacy system integrations (on-prem APIs)
- +High-reliability requirements (99.9%+ uptime)
- +Ambiguous or shifting requirements
- +Novel use cases with no established playbooks
- +Real-time latency constraints
- +Large-volume fine-tuning or evaluation loops
- −Pre-built connector libraries (LangChain, n8n)
- −Clear acceptance criteria defined upfront
- −In-house technical team available for QA
- −Standard cloud deployment (AWS / GCP / Azure)
- −Well-documented, modern REST APIs
- −Existing LLM provider contract in place
- −Dedicated stakeholder with fast approval cycles
- −Reuse of previous agent architecture
Framework Adoption by Industry
Percentage of projects in each vertical using a given framework as primary orchestration layer. Values represent share of projects, not exclusive adoption.
| Industry | LangChain | AutoGen | CrewAI | n8n |
|---|---|---|---|---|
| Healthcare | 58% | 14% | 11% | 8% |
| Finance | 49% | 22% | 19% | 6% |
| Retail | 44% | 12% | 28% | 32% |
| SaaS | 67% | 21% | 31% | 22% |
Highlighted values indicate the most-used framework per industry vertical. Projects often use multiple frameworks.