Fixed-price AI agent development: scope boundaries, eval-first delivery, guardrails, and what a real contract covers — no T&M surprises.
Keyword math: “Hire AI agent developer fixed price” is a BOFU commercial query — we estimate 60–110 monthly searches (US + EU combined), difficulty ~38–45 on a 1–100 scale. Intent is high: buyers have budget approved and are comparing delivery models. We can win because marketplaces (Upwork, PeoplePerHour) sell capacity; we sell eval-first, guardrailed delivery with a written “not building” list and a 90-day SLA. Competitors like BroCoders and Shipkit lead with ship-fast MVP messaging but rarely publish scope-change mechanics or eval gates for agents. KPI: 2 qualified AI agent project inquiries from organic in 90 days. Review date: 2026-11-21.
If you searched hire AI agent developer fixed price, you are not shopping for a freelancer to “experiment with LangChain.” You already know the problem — customer-support deflection, sales-qualification automation, internal knowledge retrieval — and you need someone who ships a production-grade agentic system with evals, guardrails, and observability built in from day one, not patched on after the demo. At Wolverine Solution (Montréal; US and EU delivery), we run fixed-price AI agent builds the same way we run any fixed-scope engagement: signed scope of work, a definition of done tied to accuracy thresholds (not “the demo works”), and a change-order process that keeps your budget intact. Stack we actually use: LangGraph, AutoGen, or CrewAI for orchestration; OpenAI Assistants API, Anthropic Claude, or local LLMs (Llama 3.1, Qwen 2.5) for reasoning; Pinecone, Weaviate, or Qdrant for vector retrieval; LangSmith or Phoenix for tracing; Terraform/Docker/GitHub Actions for reproducible deploys to AWS Bedrock, Azure AI, or GCP Vertex AI. (And no — we are not the wolverine pc game studio.)
Fixed price means a signed SOW that locks capabilities, tools, data sources, and success metrics — including accuracy thresholds on a held-out eval set — before orchestration code starts. The contract names explicit exclusions and a change-order process with fixed-day estimates. You pay for the outcome, not the hours.
Most marketplace listings and agencies treat “fixed price” as “fixed fee for a prototype.” That is not what you need. A production agent needs:
If the proposal does not name the eval dataset size, the target accuracy, and the guardrail rules, it is not fixed-price. It is T&M with a cap.
A single-agent production build with RAG, 3–5 tools, an eval harness, and a 90-day SLA typically runs $35,000–$120,000. Multi-agent workflows (for example AutoGen quote-to-cash) start near $85,000. What moves the number: data sources, write-back tools, latency SLA, compliance needs.
| Tier | Typical Scope | Eval Dataset | Guardrails | SLA | Price Range |
|---|---|---|---|---|---|
| Pilot | 1 agent, 1–2 data sources, 2–3 tools, RAG only | 200 Q&A pairs | PII, topic confinement | 90-day bug fix | $35k–$55k |
| Core | 1–2 agents, 3–5 data sources, 4–6 tools, write-back | 500 Q&A pairs | + tool-use policy, output schema | 90-day bug fix + accuracy floor | $55k–$85k |
| Scale | Multi-agent (LangGraph/AutoGen), 5+ sources, 8+ tools, human-in-loop | 1,000+ Q&A pairs | Full NeMo Guardrails suite | 180-day SLA + accuracy floor + drift monitoring | $85k–$150k |
Pricing also moves with Salesforce/HubSpot write-backs, sub-2s p95 latency, and SOC 2/HIPAA/GDPR constraints. Each tier ships with a fixed scope document and a “not building” list.
[Internal link: AI & LLM systems development services]
Included: ADR, orchestration graph, prompt versioning, vector index, CI eval gate, guardrails, observability, Terraform, runbooks, and a 90-day bug-fix SLA. Excluded unless scoped separately: fine-tuning, broad data labeling, unlisted SaaS integrations, cloud spend, legal review of outputs, and full product UI.
The “not building” list is where fixed-price lives or dies. We put it in the SOW:
Under Core/Scale we do ship: LangGraph or CrewAI graphs, embeddings via text-embedding-3-large or bge-large-en-v1.5, CI gate on F1 ≥ 0.82 (or your threshold), and an admin panel in Streamlit or Gradio — not a customer-facing product UI.
[Internal link: fixed-scope vs time and materials software development]
Before you sign, ask for three artifacts: a sample eval report with precision/recall/F1 and a confusion matrix; a guardrail config showing PII redaction, tool policy, and fallbacks; and a change-order template with fixed-day estimates. If they cannot produce these in sales, they are selling capped T&M.
Red flags in the first call:
A partner who has shipped agentic RAG for customer support deflection (target: ≥70% deflection rate) or sales qualification (target: ≥85% lead-score correlation) will pull up the LangSmith trace of a failed turn and walk you through the guardrail that caught it.
A typical Pilot lands in 6–7 weeks: discovery and eval design, core build with CI gates, eval freeze with at most two scoped fix rounds, then Terraform deploy into your cloud with runbooks. You join two 30-minute syncs per week. No ceremony tax.
Week 1–2: Discovery & Eval Design — ingest your docs/APIs, co-author the eval dataset (you provide 50–100 gold examples; we synthesize 200–400), agree on accuracy threshold, sign SOW.
Week 3–5: Core Build — LangGraph graph, RAG pipeline (hybrid search: BM25 + dense), tool definitions (OpenAPI specs → Pydantic schemas), guardrail rules, CI pipeline with eval gate.
Week 6: Eval Freeze — run full suite, share LangSmith project, you approve or request targeted fixes (max 2 rounds, scoped in SOW).
Week 7: Deploy & Handoff — Terraform apply to your AWS/GCP/Azure, Grafana/Datadog dashboards, runbooks, 90-day SLA starts.
The eval gate is the only gate that matters.
Skip fixed-price when requirements are unknown, data is inaccessible or unclean, formal certification cycles are open-ended, or you need continuous model improvement. Buy a paid discovery or a retainer instead — then convert to a fixed SOW once the eval set exists.
Concrete forks we recommend:
Paid discovery typically runs $8k–$15k and produces the eval dataset, ADR, and a fixed-price SOW for the build phase.
[Internal link: technical product leadership for early stage startup]
A Pilot-tier agent (1 agent, 2–3 tools, RAG) takes 6–7 weeks from signed SOW to production deploy. Core tier: 8–10 weeks. Scale tier (multi-agent): 12–16 weeks. Timeline assumes your data is accessible via API/export and your team responds to eval-review requests within 2 business days.
The 90-day SLA covers regression bugs (code/logic defects) and accuracy drift if the eval dataset still represents production traffic. We monitor LangSmith/Phoenix dashboards; if F1 drops >5 points below threshold on the same eval set, we investigate and fix at no cost. New query patterns require a change order.
No. Fine-tuning (LoRA/QLoRA on Llama 3.1, Qwen 2.5, Mistral) is a separate phased engagement with its own eval dataset, compute budget, and W&B/MLflow tracking. Most accuracy problems are solved by better retrieval, prompt engineering, few-shot examples, or guardrails — not fine-tuning.
Yes. We deploy via Terraform to your AWS/GCP/Azure account. You own the ECS/Cloud Run/Container Apps services, secrets, and billing. We hand over runbooks, dashboards, and the eval CI so your team can operate without a vendor lock-in portal.
Name one product owner who can freeze scope and one data/systems contact who can grant API and corpus access. Optional: a security reviewer for tool write policies. That is enough — you do not need a full agile squad on your side for a fixed-price agent build.
Ready to hire on fixed price without T&M surprises? Wolverine Solution scopes AI & LLM systems — agentic workflows, RAG pipelines, eval harnesses, and guardrails — for SMBs and early-stage founders across the US and EU. Bring the workflow you already run manually, the tools it must call, and the actions that must never auto-fire. We return a one-page SOW: capabilities, eval thresholds, exclusions, and price — before orchestration code starts. Book a scoping call.