Short answer: a narrow agent working across one or two well-documented systems typically costs $25,000 to $60,000. A production agent handling a real business process across several systems, with approval workflows and audit logging, typically runs $60,000 to $150,000. Multi-agent systems spanning legacy enterprise systems start around $150,000 and rise with integration complexity. Running costs are separate, usually $500 to $5,000 per month depending on volume.
The table below gives indicative ranges by agent type. Every figure assumes integration with existing systems and excludes ongoing model and infrastructure spend, which is covered separately below.
Ongoing costs run separately: model API spend scales with volume and agent verbosity, typically $500 to $5,000 per month for a single production agent, plus infrastructure and a maintenance retainer for evaluation, model updates, and scope adjustment.
The same process can be scoped two ways with a threefold cost difference, and the cheaper version is often the better product. The factors below are the ones worth discussing before committing to a scope.
Building interface layers over systems that were never designed to be integrated is conventional engineering work, and it frequently exceeds the agent development itself in effort.
An agent permitted to act without approval needs far more evaluation, guardrail engineering, and testing than one that prepares work for a person to approve.
Audit trails, explainability, approval routing, and compliance documentation are all necessary in regulated contexts and all add engineering time.
Automating the three highest-volume paths rather than every variation typically captures most of the value at a fraction of the cost and risk.
Keeping a person in the approval loop reduces evaluation burden substantially and is usually the right design regardless, which our agentic AI development practice recommends as the default.
Most cost reduction comes from scoping rather than from cutting corners on engineering. The approaches below reliably reduce spend while improving the chance the agent actually gets used.
A short fixed-scope proof tells you whether the process is automatable before you commit to a build, and the evaluation set it produces carries into production.
Handle the majority case well and route exceptions to a person. Chasing full coverage is where agent budgets expand without proportionate return.
If your systems already expose APIs or you already have middleware, the agent build gets considerably cheaper. Assessing that first often changes the scope materially.
Smaller models handle many agent steps adequately at a fraction of the cost, which matters most in ongoing spend rather than in the build.
Well-designed interfaces over your systems serve every subsequent agent, so the second agent costs substantially less than the first if the first was built properly.
A clear, proven path from idea to production-ready AI.
The process you want automated, the systems it touches, roughly what volume it handles, and which steps a person must approve. That is enough for an indicative range.
We review integration readiness, data availability, and control requirements, then produce an itemized estimate separating build, integration, and ongoing operating cost.
Fixed price where scope is defined, or a dedicated team where the roadmap extends across several processes. Proof of concept work is always fixed price.
Ranges reflect the integration and control work each engagement actually requires, and our pricing page explains how we structure engagements more broadly.
A proof of concept runs $10,000 to $25,000. A narrow production agent typically costs $25,000 to $60,000, a full process agent $60,000 to $150,000, and a multi-agent enterprise system $150,000 upward. Running costs are separate.
Integration, by a wide margin. The number of systems the agent must reach and their state, particularly whether they expose usable APIs, drives more cost variance than model choice, process complexity, or anything else.
Start with a proof of concept, narrow the process to the highest-volume paths, keep human approval on actions, and use any existing integrations you have. Scoping reductions save far more than engineering compromises.
Both. Fixed price where scope is defined, which suits proofs of concept and narrow agents. Dedicated team billing monthly where the roadmap spans several processes and scope evolves as evidence arrives.
Indicative only. Ranges on this page reflect typical engagements, but the integration surface dominates cost and cannot be assessed without looking at your systems. An estimate after assessment is considerably more reliable.
Model API spend scaling with volume, infrastructure, and a maintenance retainer covering evaluation, model updates, and scope adjustment. Agents need ongoing evaluation because provider model updates change behavior.
Tell us the process, the systems it touches, and roughly what volume it handles. We will come back within one business day with an indicative range and what an assessment would involve. Book a free consultation through our contact page .
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