Production AI agents — built to act, not just chat.
AI systems that plan, use your tools and complete multi-step tasks across your stack — engineered for reliability, observability and human-in-the-loop where it matters.
- Tool-using by design
- Evaluation-first
- Human-in-the-loop where it counts
- Observable end to end
Concrete outcomes, not buzzwords
Custom AI agents
Task-specific agents wired into your APIs, databases and internal tools — not generic copilots.
Multi-agent workflows
Planner / executor / critic patterns for multi-step automation with clear control flow.
Tool use & structured outputs
Typed tool interfaces, retries, validation and structured outputs the rest of your code can trust.
RAG-grounded reasoning
Agents that retrieve, cite and stay accurate — anchored in your data, not their training set.
Evaluation harness
Real test sets and regression tracking so quality improves over time instead of drifting.
Observability & guardrails
Every prompt, tool call and decision logged and traceable — with safety constraints enforced.
What we work with
Foundation models
Agent frameworks
Memory & retrieval
Observability & ops
Deployment
A deliberate sequence
Discovery
Which tasks, which tools, what success looks like — and whether an agent is the right pattern at all.
Prototype & evaluate
A narrow agent against a real test set, before scaling — so quality decisions are measurable, not intuitive.
Build to production
Tool layer, guardrails, retries, observability and cost-aware model routing — engineered, not stitched.
Scale & monitor
A/B model swaps, drift detection, eval-driven iteration. The system gets better, not stale.
Honest about cost and scope
Most agentic-AI engagements start with a 1–2 week prototype-and-evaluate phase, then a fixed-scope build typically 6–12 weeks. We write a costed proposal before any production work.
A taste of what this looks like in production
Production RAG Assistant Over a 12-Year Knowledge Base
Deflected 42% of tier-1 support tickets with cited, accurate answers.
Read case studyQuestions buyers usually ask us
When does an agent actually make sense vs a simpler LLM call?
When the task genuinely requires planning, multiple tool calls or iteration. A single-shot LLM call is cheaper, faster and easier to evaluate — and it's the right answer more often than agent demos suggest. We'll tell you honestly which pattern fits.
How do you stop agents from doing the wrong thing?
Typed tool interfaces, explicit allow-lists for actions, validation on every output, retries with backoff, and human approval gates for irreversible actions. Plus full logging so any wrong action is debuggable and recoverable.
Which agent framework do you use?
Whichever fits — LangGraph, the OpenAI Agents SDK, Crew AI, AutoGen, or custom orchestration when frameworks become a tax. We choose based on your control-flow needs, not framework fashion.
How do you handle cost at scale?
Model routing by complexity (small fast model for easy steps, frontier model only when needed), prompt caching, batching where latency allows, and aggressive caching of retrieval results. Most production agents we run cost less than people expect.
How do you evaluate agent quality?
We build an evaluation set from real tasks at the start, score retrieval and final outcome separately, and run it on every change. Without that loop, agent quality drifts silently — with it, it improves.
Can agents work across our existing systems?
Yes. Most of our agentic work integrates with existing APIs, databases, CRMs and ticketing systems — through proper typed tools, not screen scraping.
Ready to start?
Tell us about your project. We respond within one business day.