AI Agent Development Company

Build an agent when the task requires multi-step reasoning, tool use (databases, APIs, code execution), conversation state across turns, or routing between specialized capabilities. Use a direct LLM call (OpenAI/Anthropic SDK) for single-shot tasks: classification, summarization, structured extraction, single-question Q&A. Most “AI features” are actually direct calls. Agents are for workflows, not features.

Single-purpose agent MVP 6-10 weeks: 2 weeks discovery and evaluation set creation, 4-6 weeks build (prompts, tools, guardrails, eval harness), 2-4 weeks production hardening (drift detection, monitoring, rollback). Multi-agent system 4-6 months. The biggest variable is the evaluation set - building a high-quality 150-200 question eval set from real production data takes time and is non-negotiable.

Pilot agent $60,000-$120,000. Production multi-agent system $240,000-$680,000. Enterprise platform $510,000-$1,600,000. Cost drivers: number of tools the agent integrates with, evaluation set complexity, observability and audit logging, regulatory requirements, post-launch monitoring tier. The biggest hidden cost is NOT the LLM bill - it is the evaluation set, guardrails and observability you need to safely run agents in production.

Layered controls: grounded retrieval (RAG with citation tracking), structured output schemas with validation, confidence thresholds with human handoff, evaluation set tested on every deploy, runtime guardrails that flag low-confidence answers. We instrument every response so you can audit any answer back to its source documents. Hallucinations cannot be eliminated, but they can be detected, contained and recovered from.

LangChain or LlamaIndex for most production work - biggest ecosystems, best tool integrations. DSPy when we need structured prompt optimization. OpenAI Assistants API or Anthropic Claude tool use for simpler agent patterns. Sometimes no framework at all - a tight loop of LLM call + tool dispatch is often the cleanest production code. The choice depends on agent complexity, observability needs and team familiarity.

Every agent ships with an evaluation set of 150-300 questions tied to business outcomes (deflection rate, citation precision, task completion, customer satisfaction). The eval set runs on every deploy and on a nightly schedule against the production model.

For PHI (HIPAA) and PCI data: tokenization or de-identification before the LLM ever sees it; private model deployment when needed (Vertex AI private endpoints, AWS Bedrock with VPC, self-hosted Llama). Audit logging of every prompt, response and tool call. Model versioning with rollback on accuracy drift. Compliance attestations issued by accredited auditors based on the systems we deliver.

EU data subject workflows run inside the region by default (AWS Frankfurt, GCP Belgium, Azure West Europe). Personal data is redacted or tokenized before the LLM ever sees it, with the redaction key held by the client. Every agent decision that affects a natural person is logged with the full prompt, tool calls, retrieved context and final output, so we can satisfy a GDPR right-to-explanation request in minutes rather than days. For EU AI Act high-risk use cases we classify the system upfront against Annex III, document the risk profile, run bias and fairness checks on the evaluation set and maintain a version history of prompts and models so the regulator audit trail is available from day one.

We decline single-prompt features dressed up as agents (use a direct call), agents without an evaluation set tied to business outcomes (no way to know if the agent works), workflows that need zero-error guarantees on individual actions (medical dosing, financial settlement), real-time systems with sub-100ms latency budgets and projects with no plan for prompt maintenance or drift monitoring.