AI Agent Development
AI agents that finish the job.
We build production-ready AI agents that plan, use tools, and recover from failure — with evals, guardrails, and observability built in from day one.
Task success rate
92%+
Target on evaluated workflows after launch
Tool integrations
Any API
REST, GraphQL, SQL, SDK, legacy SOAP
Memory horizon
Long
Context preserved across multi-hour and multi-day runs
Deployment model
Cloud or VPC
SOC 2 and government-ready architecture
What our agents do
Planning and reasoning
ReAct and tree-of-thought planners that decompose complex requests, backtrack when stuck, and choose the right tool for each step.
Tool use and routing
Secure tool calls to your internal systems with retries, timeouts, fallbacks, and structured output validation at every step.
Long-horizon memory
Vector + structured memory so the agent recalls earlier context, user preferences, and prior decisions across sessions.
Human-in-the-loop
Escalation checkpoints where the agent pauses for approval, correction, or sensitive decisions — with full context attached.
Guardrails and evals
Pre-launch eval suite plus runtime guardrails for cost, safety, PII, and policy compliance on every run.
Observability by default
Every plan, tool call, and result is traced. Debug failures, measure task-success deltas, and improve continuously.
Where teams deploy them first
Claims and case processing
Read documents, verify policy rules, request missing data, and update case status — reducing hours of manual review to minutes.
Vendor and supplier onboarding
Collect documents, run checks, create records across ERP and CRM, and follow up automatically until onboarding is complete.
IT and operations triage
Ingest alerts, query logs and runbooks, attempt remediation, and escalate with a full incident summary when needed.
Government service delivery
Guide citizens through multi-step applications, validate inputs against registries, and schedule follow-ups without adding headcount.
Custom agents vs. generic assistants
| Capability | Premium Robots | Generic assistant |
|---|---|---|
| Planning depth | Multi-step reasoning | Single-turn prompts |
| Tool integration | Custom internal APIs | Pre-built connectors only |
| Failure recovery | Retries + human checkpoint | Stops on first error |
| Eval before launch | Required | Rarely included |
| Traceability | Full audit trail | Prompt logs only |
Frequently asked questions
- What is an AI agent?
- An AI agent is a system that can break a goal into steps, call tools or APIs, remember context across a long session, and recover when a step fails. Unlike a chatbot that answers one question at a time, an agent completes end-to-end tasks such as processing a claim, onboarding a vendor, or reconciling a report.
- How is this different from a chatbot or copilot?
- Chatbots answer questions; copilots assist a human. Agents act. They make decisions, retry failed steps, call internal systems, and continue across hours or days until the job is done — with a human only at defined checkpoints.
- What tools can the agents use?
- Any REST, GraphQL, SQL, or SDK-backed system you already run: CRMs, ERPs, ticketing systems, databases, document stores, email, calendars, and internal APIs. We design the tool layer so adding a new capability is a configuration change, not a rewrite.
- How do you ensure reliability?
- Every agent ships with an eval suite that tests normal paths, edge cases, and failure modes before release. In production we add guardrails, structured output validation, cost-aware model routing, and full traceability so you can debug any run.
- How long does a pilot take?
- A focused single-workflow pilot typically goes live in 4–6 weeks, including discovery, tool integration, eval harness, and a staged rollout. Multi-workflow agent platforms run 10–14 weeks.
Scope your first agent
Send us one workflow and we will map the tools, define the eval suite, and propose a pilot with measurable success criteria.