AI Agent Development Services for US & EU Operations and Product Teams

Default to a single LLM call. Move to an agent only when the task has three properties: it requires multiple tool calls whose order cannot be hardcoded, it operates over state that changes across turns, and the success criterion is verifiable enough that the agent can self-correct. Customer support triage is rarely an agent; ops workflows that touch four internal APIs in a different order each time often are. We refuse agent projects where a deterministic pipeline plus one LLM call would ship in half the time with a quarter of the bugs.

Depends on the workload. LangGraph for stateful agents with branching control flow and human checkpoints — the explicit graph is worth its weight when you debug at 2am. CrewAI or AutoGen when multi-agent collaboration is the actual pattern (rare). OpenAI Assistants when the workload is tightly coupled to OpenAI's tool format and you do not need portability. Temporal or Inngest when the agent is really a durable workflow with LLM steps inside. Vercel AI SDK for Next.js front-ends with simple tool use. We pick on operational fit, not vendor preference.

Three controls. Hard per-task token and dollar budgets at the orchestration layer — the agent terminates with a clear error before it loops 40 times into your OpenAI bill. Step-level tool-call validation through Pydantic so invalid arguments are caught before the API call, not after. Human-in-the-loop checkpoints on irreversible actions (sending email, posting to production, charging a card). Observability through Langfuse, Helicone, or Arize logs every step, every tool call, every token. Cost alerts fire on the orchestrator, not the dashboard you check on Monday.

Memory is three things, not one. Short-term: the current conversation buffer, summarised when it exceeds context budget. Long-term episodic: facts the agent learned about the user or task, stored in a vector store with semantic recall (pgvector or Weaviate). Long-term semantic: the corpus the agent retrieves from, treated as a RAG subsystem. We size each tier explicitly because naively cramming everything into the context window costs five to ten times more per request and degrades quality. Per-agent memory cost is typically 30 to 60 percent of the LLM cost when designed; 300 percent when not.

Tiered checkpoints. Tier 1 (autonomous): read-only actions, no human gate. Tier 2 (async review): a human sees and can revert within a window, but the agent does not block. Tier 3 (sync approval): irreversible actions (sending email, posting to production, charging) wait on human approval before execution. The approval UI is part of the deliverable, not an afterthought — usually a Slack interactive message, a queued action in your existing admin, or a custom approval inbox. Tier assignment is per tool, written down, and changes through PRs not Slack.

Four fixed-scope tiers, all-in and quoted in USD. A task/PoC agent runs from $2,900 (2–3 weeks): a single-task agent or a working proof of concept. A RAG-grounded agent from $8,100 (4–6 weeks): retrieval and grounding over your corpus with citations. An autonomous AI agent from $10,400 (6–9 weeks): tool orchestration, memory, human-in-the-loop and observability. A multi-agent or ML-in-production system from $13,800 (9–13 weeks). You see the line-item budget at the end of discovery and sign off before any code is written. No recruitment markup, no tool surcharges; cloud fees run on your own accounts, so you keep the cost lever.

You do, from day one. IP assignment is signed before the build starts, so the code, the prompt library, the eval set, the tool schemas and the orchestration config are all yours. We hand over the repository, the infrastructure-as-code and the runbooks; there is no proprietary runtime you have to keep licensing from us to keep the agent alive. Cloud and model accounts are yours too, which is why you keep the cost lever rather than paying a marked-up passthrough.

Yes. The provider layer is deliberately model-agnostic, so if you are standardised on Azure OpenAI, Anthropic on Bedrock, or Google Vertex for contractual or residency reasons, the agent routes through it without a rewrite. For GDPR-aligned work we keep the data plane in your chosen region, document every place data crosses a boundary, strip or tokenise PII before it reaches a model where that is required, and can run retrieval and memory entirely inside your VPC. Compliance scope (GDPR, HIPAA-capable, PCI DSS) is priced in, not bolted on.

Every engagement past the PoC tier ships an eval harness, and it runs in CI on each prompt or tool change — not as a one-off demo. We build a graded test set from real tasks, score correctness, tool-call validity, groundedness (for RAG) and cost per task, and gate deploys on regressions. Production rollout is a gradual traffic ramp behind a feature flag with cost and latency SLOs, so the first real users are a controlled slice, not the whole queue. An agent that cannot be measured is an agent we will not put in front of your customers.

Often, yes. We start with a short audit: where control flow is emergent instead of explicit, where tool calls lack validation or idempotency, where cost runs away, and whether memory is cramming everything into the context window. You get a written findings list with the honest call — refactor the existing graph, or rebuild the risky parts. Many rescues are less about the model and more about the missing operational scaffolding: budgets, checkpoints, traces and evals that were never there.

Defence in depth, because no single control is enough. Tool arguments are validated through strict Pydantic schemas before any API call, so a manipulated instruction cannot smuggle a malformed or out-of-scope action through. Irreversible tools sit behind sync-approval checkpoints regardless of what the model “decided”. Retrieved and user-supplied content is treated as untrusted data, not as instructions, and kept out of the privileged system prompt. Least-privilege credentials mean a compromised step cannot reach systems it was never scoped for, and every step is traced so misuse is visible after the fact.

That is the goal of the rollout phase, not an afterthought. You get the repository, infrastructure-as-code, the eval set, observability dashboards and a runbook covering stuck agents, tool outages and cost spikes. We train your engineers on adding tools, expanding the eval set and reading the traces, then step out when your team is operating the agent day to day. If you want ongoing help, support is a separate agreement with a defined SLA — a choice, not a dependency we engineer in.