AI Agent Development.
Custom agent design, multi-agent orchestration, tool use, hand-off logic, evaluation pipelines. Built to ship in production, in regulated environments, with audit trails your CISO can defend.
Agents that act, with humans in the loop where it matters.
An “AI agent” in 2026 means something specific: a system that reads context, makes a plan, calls tools, observes results, and adjusts — with human escalation built into the decision graph. We build agents that work that way, not chat widgets dressed up to look agentic.
Our default delivery shape includes: a clear agent contract (what it can decide, what it must escalate), explicit tool definitions (with schema, auth, rate limits), an evaluation harness that runs against your actual workflow, and an audit pipeline that records every decision with the prompts and reasoning that produced it.
We routinely build single-agent systems and multi-agent systems with explicit hand-off (planner → executor → reviewer patterns, for example). The right shape depends on your workflow, your compliance posture, and your tolerance for autonomy.
What you receive at the end of an Agent Development engagement.
Agent contract document
Plain-English description of decision scope, escalation triggers, audit expectations.
Architecture diagrams
Tool graph, hand-off graph, integration surface, data flow. CISO-ready.
Production codebase
Repo with IaC, CI/CD, test suite, and runbook. Yours, not ours.
Evaluation harness
Replayable test suite over real workflow examples. Drift detection ready.
Audit trail spec
Log schema, retention policy, SIEM-feed configuration.
Team handover doc
Operating instructions, common failure modes, escalation playbook.
We build with what works, not what's trending.
Typical stack: Anthropic Claude or open-weights models (Llama 3.3, Qwen 2.5, DeepSeek-V3, Mistral) running through our agent framework. Tool definitions over MCP where applicable; traditional function-calling otherwise. Vector retrieval via Postgres + pgvector, Pinecone, or your existing system. Audit logging via OpenTelemetry into your SIEM.
Frameworks: we use LangGraph and the agentic primitives from the Anthropic and OpenAI SDKs where they fit, custom orchestration where they don't. We've contributed to several of the major open-source agent frameworks; we know their seams.
For multi-tenant agent hosting with chat as the interface, we build on Ethora — our own platform. That's what powers the personas on this site, in fact.
Discovery Sprint $15-30K. Production build $200-280/hr blended.
See /pricing/ for all tiers. Most engagements start with a 2-3 week Discovery Sprint.