We design & engineer
AI Agent Workflows
that actually work.

Break complex tasks into sequential steps where each LLM call feeds into the next. Ideal for structured, predictable pipelines — draft → review → reformat.

A classifier decides which specialized sub-agent or tool handles a request. Power customer support bots where queries route to billing, tech, or sales agents.

Multiple agents run simultaneously on sub-tasks, then results are aggregated. Sectioning splits large tasks; voting runs the same task N times for reliability.

A manager LLM decomposes a goal and dispatches subtasks to specialist workers. The orchestrator synthesizes results — common in coding agents.

One agent generates output, another evaluates against criteria, and the generator refines based on feedback. Repeats until quality threshold is met.

Reason + Act. The agent interleaves reasoning steps with tool calls — think, act, observe, repeat. The backbone of most tool-using agents.

A planner LLM produces a full step-by-step plan upfront, then an executor works through it. More deterministic than ReAct; ideal for well-defined tasks.

Retrieval-Augmented Generation — query → retrieve relevant chunks from a vector store → augment prompt → generate. Standard for enterprise knowledge Q&A.

Agents maintain short-term, long-term, and episodic memory across sessions. Used in personal assistants and CRM-integrated agents that learn over time.

Multiple agents with different personas argue and critique each other's outputs. Improves factual accuracy and reduces hallucination on complex reasoning tasks.

Agent handles the task autonomously but pauses at critical decision points for human review or approval before proceeding. Essential for compliance, finance approvals, and medical triage workflows.

Agent detects its own failures — errors, bad outputs, timeouts — diagnoses the cause, and autonomously retries with a corrected strategy. Used in data pipelines, automated testing, and API integrations.