Agentic AI — Autonomous Workflows for Production
Most teams hit an efficiency ceiling: every new tool means another person copy-pasting between tabs. Agentic AI is digital labor — autonomous workflows that span your tools, run on your rules, and scale without hiring.
What you get
When it fits
- The workflow spans more than one system and has a clear, instrumentable success metric
- You can name the 5–10 actions the agent should be allowed to take
- Failure has a recoverable cost — humans can review or undo within a reasonable window
- You want to remove repetitive labor, not eliminate human judgment from a high-stakes decision
When it doesn't
- The 'agent' is really a chatbot — single-turn Q&A doesn't need agency
- The decisions are irreversible and high-stakes (medical diagnosis, legal judgment) without human sign-off
- Your tools don't have APIs and there's no appetite to build the integration layer
Process
We start by mapping the workflow and writing the tool contracts before any code. Sprint 1 ships a narrow vertical slice with full observability. Sprints 2–4 expand the action set, tighten guardrails, and roll out behind a feature flag. Evaluation is continuous — every release is gated on the eval harness, not on whether it 'looks right'.
Full delivery processPricing
Typically a fixed-price 8–14 week build, or an AI Pod (4–7 specialists) on a quarterly engagement. Outcome-based pricing where the metric (tickets resolved, documents processed, hours saved) is instrumentable.
See engagement modelsCase studies
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FAQ
- How is this different from just using a chatbot?
- Chatbots answer questions. Agents take actions: filing tickets, updating records, calling APIs, kicking off workflows. The engineering challenge is making those actions reliable and reversible at scale, which is where most agent projects fall over.
- Which frameworks do you use — LangChain, CrewAI, AutoGen?
- Whichever fits the problem, but the framework choice rarely matters as much as the architecture decisions around it: tool boundaries, eval design, and observability. We're framework-agnostic and have shipped on all of the above plus pure orchestration code where the framework was the bottleneck.
- How do you stop an agent from doing something stupid?
- Three layers. First, scoped permissions — agents only see the tools they need. Second, action gates — high-impact actions require human approval until the eval harness shows they're safe. Third, a kill switch and rollback runbook in production from day one.
- How do you measure whether an agent is actually working?
- Workflow-specific success rate, cost per successful action, escalation rate to humans, and end-to-end latency. We define these with you in discovery and instrument them in production from sprint one — not after launch.