AgentKIT
Autonomous agents for business workflows. Agents with tools, memory, and reasoning — deployed into support, sales ops, and internal tooling.
AgentKIT is a production agent runtime for teams that have outgrown single-prompt chatbots and need autonomous workflows. It solves the awkward middle of LLM products — tool orchestration, durable state, failure recovery — so your engineers focus on domain logic instead of plumbing. Built for operations, support, and revenue teams running on ticketing systems, CRMs, and internal APIs.
A planner/executor loop drives typed function calls against your tools, with each step persisted to a durable queue (Temporal) for replay and observability. Models are pluggable across OpenAI, Anthropic, and open-weight endpoints; retrieval hooks into your existing vector store or KnowledgeKIT. Human-in-the-loop gates, policy checks, and prompt-injection defenses sit in front of every tool call, and OpenTelemetry traces stream to your existing dashboards.
On day 1 you get a working agent against your tools with generic policies, a trace UI, and an eval harness seeded with a handful of golden tasks.
By week 3 the agent runs on tuned prompts, tenant-scoped memory, custom tool schemas, and an eval suite graded against your real transcripts — with canary rollout and on-call dashboards in place.
Build it from scratch, or start a week ahead
- Six to nine months building the orchestration, memory and recovery before a first real result.
- An evaluation harness you write from scratch — then argue about.
- A team learning your edge cases live, in production.
- A black-box vendor you can't inspect, tune or move off.
- A working AgentKIT on your data in week one — the hard parts already solved.
- Evals seeded on day one and graded against your real workflows.
- Senior owners with full traces and dashboards from the first deploy.
- You own the prompts, the weights, the traces and the outcomes.
Ships with the hard parts solved
Tool use & function calling
Typed tools, retries, and sandboxed execution across your APIs and databases.
Long-term memory
Episodic and semantic memory with retention policies scoped per tenant.
Planner / executor loop
Reasoning traces you can inspect, replay, and evaluate offline.
Guardrails & evals
Policy checks, prompt-injection defense, and CI evals on every change.
Where teams deploy it
Fine-tuned on your data and shaped to the workflow it lands in — these are the deployments we see most.
Kick-off to production in three weeks
A fixed scope and a visible finish line — you see it working before it's load-bearing.
Week 1 — Integration
We wire AgentKIT into your identity, data sources, and tool APIs.
Week 2 — Fine-tune
Policies, tool schemas, and evals are tuned on your workflows and transcripts.
Week 3 — Ship
Canary rollout with traces, dashboards, and an on-call playbook.
Building an agent runtime from scratch is a 6–9 month detour that most teams underestimate. AgentKIT gives you the hard parts — durability, evals, guardrails, traces — on day one, so the only thing you build is the part only you can build: your domain.
“They understand our needs quickly and are a delight to work with.”
AgentKIT, live on your stack in a week
One call to scope it, a senior team on it from day one — and a fixed scope agreed before we start.