Autonomous agents that take action, not just generate text.
Most enterprise AI projects stop at a chatbot that answers questions. Agentic AI goes further — systems that plan multi-step tasks, call tools and APIs, retrieve and reason over your data, and take action within defined guardrails, escalating to a human only when genuinely needed. We design and build these agents on top of your existing systems, from customer-support automation to internal workflow agents that handle the repetitive decisions your team currently makes by hand.
Discuss Your ProjectAgents that call APIs, query databases, and complete multi-step tasks — not just answer questions.
Clear escalation triggers and human-in-the-loop checkpoints for anything high-stakes or ambiguous.
RAG pipelines and tool integrations keep agents grounded in your actual systems, not just model training data.
Identify tasks with clear success criteria and defined boundaries — not open-ended automation.
Agent architecture, tool access, and the guardrails that define when to escalate to a human.
Iterative development against a test suite that measures task success, not just plausible-sounding output.
Staged rollout with monitoring for drift, failure modes, and cost per completed task.
AI & Machine Learning
The DevOps discipline that keeps your ML models working in production.
AI & Machine Learning
Language models that understand your customers, automate your documents, and scale your knowledge.
Common questions about our AI Agents & Agentic Solutions service.
A chatbot answers questions in a conversation. An agent plans and executes multi-step tasks — calling APIs, querying systems, and taking action — often without a human directing each individual step. The distinction matters because agents carry real operational risk that a chatbot answering FAQs does not.
Tasks with clear success criteria and bounded scope: triaging support tickets, reconciling data across systems, drafting and routing approvals, or monitoring for specific conditions and escalating. Open-ended, judgment-heavy decisions are still better handled by a human with AI assistance rather than full autonomy.
Guardrails are designed before the agent is built, not bolted on afterward — scoped tool permissions, confirmation steps for irreversible actions, and explicit escalation triggers for anything outside defined confidence thresholds. We treat "the agent should ask a human" as a designed feature, not a failure mode.
We are provider-agnostic and select based on the task — Anthropic's Claude models for tasks requiring careful tool use and instruction-following, alongside other providers where a specific capability or cost profile fits better. We avoid locking clients into a single vendor by default.
Task success rate against a held-out evaluation set, not just qualitative review of a few examples. We build the evaluation framework alongside the agent itself, so you have an objective measure before wider rollout, and can catch regressions when the underlying model or prompts change.
Yes — this is usually the majority of the engineering effort. We build the tool integrations and, where relevant, MCP servers that let agents securely read from and act on your existing systems, rather than operating in an isolated sandbox disconnected from real data.
It escalates to a human by design, with full context of what it attempted and why it stopped. We treat unhandled cases as expected input, not exceptions — every agent we build has a defined fallback path rather than failing silently or guessing.
Our team will scope your requirements and come back with a clear proposal within 48 hours.