// services/ai-agents
Autonomous agents engineered for production
We design and build autonomous AI agents that reason over your data, call your tools, and complete real workflows — with evaluation, guardrails and observability baked in from day one.
// the_problem
Agents that plan, act and deliver
Chatbots answer questions. Businesses need software that completes work. The gap between a impressive demo and an agent you can trust in production is engineering: reliable tool use, bounded autonomy, evaluation harnesses, and failure recovery.
// deliverables
What we hand over
Agent architecture design
System diagrams, tool boundaries, memory strategy and model routing mapped to your workflows before a line of code.
Production agent runtime
Deployed agents with retries, timeouts, budget caps and full execution traces — observable like any other service.
Eval & safety harness
Regression suites for agent behavior, red-team scenarios, and escalation paths so humans stay in control.
- Multi-step planning and tool-use agents
- Multi-agent orchestration systems
- Tool and function-calling pipelines
- Evals, guardrails and human-in-the-loop review
- Tracing, observability and cost controls
- Long-running background agent workers
// faq
Questions we hear a lot
Which models do you build on?
We are model-agnostic. We typically build on frontier models from OpenAI and Anthropic, with routing layers so you can swap models as the landscape changes without rewriting your product.
How do you prevent agents from taking wrong actions?
Layered guardrails: schema-validated tool calls, permission scopes per tool, spend and step budgets, deterministic checks on outputs, and human-in-the-loop approvals for high-stakes actions.
How long does a typical agent project take?
A focused single-workflow agent usually ships in 4–8 weeks. Multi-agent systems with complex integrations run 8–16 weeks depending on surface area.