Real numbers from an agency that builds agents for a living: what production AI agents cost, why quotes vary 10x, and where the money actually goes.
Fixed-price scopes · 4-8 week typical delivery · 100% code ownership
Typical timeline: 3-4 weeks
Examples: Email or ticket triage, document extraction, lead qualification, report generation
Includes: One well-defined workflow, 1-3 tool integrations, evaluation suite, human-review fallback
Typical timeline: 5-7 weeks
Examples: Back-office pipelines that plan and execute across systems: CRM updates, invoicing chains, research-and-draft workflows
Includes: Planning + tool-calling loops, 3-8 integrations, audit trails, cost controls, staged rollout
Typical timeline: 8-12 weeks, phased
Examples: Department-wide automation with multiple agents, SSO, role-based approvals, and compliance requirements
Includes: Multi-agent orchestration, SSO/RBAC, human-in-the-loop approval flows, observability, SLAs
An agent that answers from a knowledge base costs a fraction of one that plans multi-step actions across five systems. Each additional decision point adds evaluation and guardrail work.
Every system the agent touches (CRM, email, ERP, internal APIs) adds connection, permissioning, and failure-handling work. Integrations are the most underestimated line item.
Moving from 90% to 99% reliability is where most of the engineering lives: evaluation datasets, regression suites, and human-review checkpoints for the remaining edge cases.
Budget for inference: a production agent typically runs $50-$2,000/month in model costs depending on volume. Good engineering (caching, model routing, prompt design) cuts this 3-10x.
In 2026, a production-grade AI agent costs $8,000-$20,000 for a single-purpose agent, $20,000-$45,000 for a multi-step workflow agent, and $45,000+ for enterprise platforms with SSO, approvals, and compliance. Prototypes are cheaper but rarely survive contact with real data. Most of the cost is reliability engineering, not the first demo.
Model inference typically runs $50-$2,000/month depending on volume, plus hosting (often under $100/month on serverless platforms). Well-engineered agents use model routing and caching to keep inference costs 3-10x lower than naive implementations.
If you have engineers experienced with LLM tool-calling, evaluation, and production guardrails, yes. If not, the hidden cost is the 3-6 months of iteration to reach reliability. An experienced agency ships in 4-8 weeks with the failure modes already designed out, which usually costs less than the in-house learning curve.
Low quotes usually price the demo; high quotes price the production system. Ask any agency what their quote includes for evaluation suites, guardrails, audit logging, and post-launch iteration. That's where the difference lives.
Want the technical background first? Read our practical guide to building AI agents or see the full AI agent development services we offer.
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