AI Agent Development Company for Enterprise Automation

Fixed-scope agent MVPs we ship start at $25,000 and ship in 6 to 10 weeks. Production agent platforms with multiple tools, vector memory, eval, and observability run $80,000 to $250,000. Enterprise multi-agent systems with audit, role based access, and human approval workflows start at $300,000. Or hire a dedicated senior AI agent engineer monthly from $2,000, first week on us.

A focused single-task agent MVP ships in 6 to 10 weeks. Production agent platforms with multiple tools, memory, eval, and observability take 12 to 20 weeks. Enterprise multi-agent systems run 5 to 9 months. The eval suite runs on every PR from sprint one and gates every release.

A golden eval set is authored before the first prompt. Ragas and DeepEval scores gate every release. Guardrails on tool calls, hard rate limits, cost ceilings, allow lists on outbound URLs and recipients, output validators, fallback paths on tool failure, human approval on high-impact actions, and a full audit log of every tool call and message.

Yes. Function calling with strict JSON schemas, custom Python tools, OpenAPI specs, and Anthropic MCP servers. We have shipped agents wired to Salesforce, HubSpot, Jira, GitHub, Okta, Stripe, Snowflake, Slack, and dozens of internal REST and GraphQL APIs.

A chatbot answers, an agent acts. Agents plan, choose tools, take actions in the world, observe results, and re-plan. Workflows execute fixed steps. Agents decide which steps to take. We use LangGraph state machines so the path the agent takes is observable, testable, and recoverable on failure.

Golden eval set with task-level success criteria. Ragas for retrieval quality, DeepEval and AgentBench for agent loop quality, custom evals for your domain rules. LangSmith and AgentOps traces in CI. Production sampled traces reviewed weekly. The regression suite blocks deploy if scores drop.

Yes. Every action with real-world impact, send email, post to CRM, write to a production database, can require human approval. We ship Slack and inbox approval UIs out of the box. Auto-approval thresholds can be raised as eval scores climb in production.

Both. MCP for GitHub, Slack, Postgres, filesystem, and other supported targets. Custom Python tools with strict JSON schemas for everything else. Tool selection routed through a typed function-calling layer so the agent cannot invent arguments or call tools it does not have access to.

Full trace of every step, prompt, tool call, response, and token cost in LangSmith or AgentOps. Helicone or Langfuse for spend dashboards. Arize for drift on retrieval. Daily cost report, weekly trace review meeting, instant alerts on tool failure or runaway loops, and a published SLO on agent task success rate.

Claude 3.5 Sonnet and 3.7 Sonnet are our default for tool use, function calling reliability, and long agent loops. GPT-4o is strong on vision and broad reasoning. Gemini 2.0 Flash is the cost play for high-volume light tasks. Llama 3.1 for on-prem deployments. We benchmark on your eval set and let the numbers decide.

Yes. Mutual NDA before any technical discussion. We can use our template or sign yours. Typically turned around within 24 hours. You can talk to a senior agent engineer the same day.

Yes. Your GitHub, GitLab, or Bitbucket org owns the repository from day one. Our engineers push commits as named contributors. Prompts, tool definitions, eval sets, and trace data all live in your repo and your accounts. Full IP transfer in every SOW.

Yes. Daily standup in your time zone. Overlap with US Eastern, US Pacific, UK, EU, UAE, and Australian timezones. Dedicated engineers shift their hours to match yours on long engagements.

Senior engineers only, no bait and switch. NDA in 24 hours. Code in your repository from day one. 7-day no-risk trial. ISO 9001 process discipline. Direct senior engineer access, no project manager filter. Transparent monthly pricing. 11+ years shipping software in production, 350+ builds across 35+ countries.