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Move from AI-generated answers to controlled workflow execution.
AIREC develops AI agents and automation systems that work across information sources, application interfaces, and business processes.
We combine model-assisted interpretation with explicitly defined tools, workflow logic, and execution controls. The goal is to automate suitable tasks while keeping responsibilities and decision boundaries clear.
An agent should have a clear task, access to relevant information, and a bounded set of permitted actions.
We begin by identifying which steps require language understanding or flexible interpretation and which should follow fixed business rules. We then define where the system can proceed automatically, where it must ask for clarification, and where human approval is required.
Capabilities & Practice
We develop agents that can retrieve information, call approved APIs, prepare structured outputs, and coordinate defined tasks.
Tool interfaces specify accepted inputs, available actions, and expected results. Permissions and action validation are enforced in the surrounding application rather than relying only on instructions given to the model.
We connect AI-assisted steps with deterministic processing, business rules, and system integrations.
Workflows can include triggers, routing decisions, persistent state, approval stages, and asynchronous processing. We define how work resumes after an interruption and how completed actions are tracked to reduce unintended repetition.
We connect agents with the systems required for the workflow, such as internal databases, document repositories, ticketing platforms, or customer-management tools.
Integrations use scoped credentials and explicit access boundaries. The agent receives only the interfaces and information needed for its assigned responsibilities.
We design review points around the consequences of each action.
A workflow might automatically classify a request or assemble a draft while requiring approval before sending an external message, changing a sensitive record, or executing an irreversible action. Approval history and execution outcomes can be recorded for review.
We define timeouts, retry limits, fallback paths, escalation rules, and execution budgets.
Retrieved content and tool responses are treated as untrusted inputs. We test how the system handles missing information, malformed responses, conflicting instructions, and attempted actions outside its permissions.
We evaluate the workflow at the task level.
Measures can include successful completion, correct tool selection, argument validity, approval compliance, recovery from tool failures, processing time, and cost per completed task.
Execution traces capture tool calls, relevant state changes, approvals, and outcomes, with logging controls appropriate to the data involved.
Potential applications include request triage, document review coordination, internal information gathering, report preparation, and cross-system administrative workflows.
The automation boundary is set by the process, available interfaces, and acceptable risk, not by an assumption that every step should be autonomous.
Typical deliverables include workflow specifications, agent and orchestration code, tool integrations, permission and approval rules, evaluation scenarios, execution logging, and deployment documentation.
Build agents with explicit responsibilities, measurable outcomes, and an operational design your team can inspect.
Not necessarily. We start with the simplest architecture that can meet the requirements. Additional agents are introduced only when separating responsibilities provides a clear, testable benefit.
Some authorized, low-risk steps can be automated. We define approval requirements for sensitive or consequential actions during workflow design rather than treating autonomy as an all-or-nothing choice.
We first assess existing models and focus on tool design, context management, orchestration, and evaluation. Model adaptation is considered only where it addresses a demonstrated limitation.