AI agent development company.
Production agents. Not Loom demos.

We design and ship autonomous agents that complete multi-step work against your systems — not chatbots that only answer questions. A production AI agent reads a request, plans the steps, calls tools (CRM, ERP, ticketing, search, your own APIs), observes the results, and either completes the task or escalates with a redacted draft. As an AI agent development company we own the full stack: eval set, architecture pick (ReAct, plan-and-execute, hierarchical multi-agent), tool integration, model selection per step, observability, and the continuous-improvement loop afterwards. Most engagements run 4–6 weeks for a first production agent, then continuous monthly for the next ones. Agents typically slot into our broader end-to-end AI development engagements, with agent governance and guardrails wired in from day one. Where the workflow is deterministic enough that a reasoning loop is overkill, we ship it as agentic automation instead.

Honest answer: it depends on stack depth and TCO. Platforms (Lindy, Sierra, Cognition, and the agent layer inside HubSpot / Salesforce) win when your stack is mostly SaaS, your flow is templated (FAQ deflection, lead enrichment, calendar booking), and your team is under 50 people. Custom AI agent development wins when you have real integration depth (legacy ERP, internal APIs, custom auth), regulatory requirements (HIPAA, SOC 2 with audit logs you own), or three-year TCO matters more than week-one velocity. We're an AI agent development company that does only the second case — and we'll tell you when the first is the right call. Roughly 1 in 5 audits we run end with "buy the platform, here's the SOW for integration."

Three engagement shapes. (1) AI agent audit: fixed-fee, 1–2 weeks. We map your workflows, identify which 1–3 are agent-shaped, recommend a recipe per workflow, project token + run-cost per agent. You leave with a written roadmap. (2) AI agent pilot: fixed-bid, 4–6 weeks for one agent shipped end-to-end with an eval set, integration, observability, and shadow-mode metrics. (3) Continuous AI agent team: monthly, embedded squad shipping subsequent agents on cadence with drift + cost reporting. These are the same engagement shapes across all our AI development services pillars. Per-workflow run cost typically lands at $200 to $1,500 per month depending on volume and model tier.

A chatbot answers questions in a conversational turn. An AI agent completes tasks: it plans, calls tools, writes to your systems of record, and recovers from errors without a human turn between every step. The two are not mutually exclusive: many of our production agents have a chatbot interface on the front and an agent loop on the back. The buyer-intent split matters because chatbots are mature (most teams need an AI chatbot development partner more than an agent specialist), whereas agents are still where the engineering risk lives. If you're not sure which you need, we cover that in the audit. About 40% of "we want an agent" inquiries are actually well-served by a chatbot plus 2–3 hardened workflow integrations.

Both. AI agent consulting services is a real engagement on its own: the fixed-fee, 1–2 week agent audit. We walk through your workflows, recommend which are agent-shaped (and which are not), pick the recipe (ReAct, plan-and-execute, hierarchical), and project the run-cost. You leave with a written plan; you can build with us, in-house, or hand the spec to another agency. About 60% of audits move into a build engagement with us; the other 40% take the plan and run it themselves, which is fine. The audit is fixed-fee for that exact reason: we shouldn't be incentivized to recommend a build that isn't there.

All three, picked per workflow. LangGraph is our default for stateful, observable agents: best graph semantics, best Langfuse integration, easiest to debug at 2am. CrewAI fits hierarchical multi-agent setups where role decomposition is clean (researcher / writer / reviewer). AutoGen we use for research + analyst agents where Microsoft's conversational pattern is a natural fit. We also ship on the OpenAI Agents SDK and Anthropic's tool-use API directly when the workflow is simple enough not to need an orchestration layer. We're framework-agnostic and we'll tell you when not to use a framework at all. About 20% of agents we ship are pure Python + a tool registry, no LangGraph.

Yes. This is one of our most-requested patterns. The fear is real (every team has seen the autoreply screenshots that went viral). The fix is eval-first: we build a rubric covering grounding (is the answer in your KB or order data?), tone (does it match your brand voice?), and policy (does it apply credit/refund rules correctly?). Every draft is scored before send; below threshold escalates to a human with a redacted draft. We add hard guardrails on refunds, account changes, and anything that touches money. Typical customer service AI agent ships at 40–50% tier-1 deflection with zero refund errors in the first 60 days, because the agent is honest about what it doesn't know.

When a workflow automation (no agent loop, just a deterministic pipeline) would do the job. Roughly 30% of "build us an agent" inquiries are really workflow problems: invoice-to-PO matching with stable rules, lead routing, ticket categorization. These don't need a reasoning loop; they need a clean ETL + LLM-as-classifier. We have a sibling service for that (AI automation) and we'll route you there if the audit says so. We'll also push back if your workflow has under 200 events/month (the agent's amortized build cost won't beat manual work) or if your team doesn't have the operational maturity yet to own an LLM in production (the agent will drift and nobody will notice).