Flow and state
Stages, decisions, required memory, stopping conditions, retry rules, and treatment of failures.
AI agentsAgents that operate with context and control.
We build systems that retrieve knowledge, use tools, execute multi-step workflows, and request approval when needed. The objective is not a convincing demo. It is an observable, testable operation aligned with task risk.
Real toolsAPIs, datasets, documents, and systems.
Human controlApproval at critical points.
Continuous evaluationTraces, cases, and operational criteria.
Safe operationPermissions, limits, and observability.
Use cases
Automation with judgment, not blind sequencing.
Agents make sense when a task requires interpretation, search, tool selection, and adaptation. For deterministic flows, simple automation is usually safer and cheaper.
Knowledge operations
- Internal assistants over documents and policies
- Request triage and enrichment
- Research, synthesis, and report production
- Service with system lookup
- Review and approval workflows
Engineering and product
- Code review agents
- Back-office automation through APIs
- RAG with citations and access control
- Multi-step orchestration
- Domain-specific copilots
Architecture
The parts that make an agent operable.
The model is only one component. Behavior emerges from the contract among tools, state, permissions, recovery, and evaluation.
Tools and integrations
Validated APIs, controlled permissions, idempotency, clear limits, and integration tests.
Knowledge and RAG
Ingestion, retrieval, citations, updates, access control, and response-quality evaluation.
Quality harness
Traces, case banks, evaluators, deterministic checks, and gates for validated versions.
Implementation
From hypothesis to production.
Each phase must reduce a concrete uncertainty: usefulness, quality, integration, safety, or cost.
Select the task
Volume, variability, value, risk, data, and the conditions under which the agent must refuse or escalate.
Prototype with traces
A minimal flow, simulated tools, and complete observation of decisions before critical integrations.
Evaluate the system
Real and adversarial cases, quality by dimension, permissions, cost, latency, and recoverable failures.
Operate within limits
Gradual rollout, human approval, alerts, audit trails, and continuous review of the case bank.
Frequently asked questions
Before automating
Is every chatbot an agent?
No. An agent typically maintains state, chooses actions, and uses tools to advance a task. A question-answering chat may be better implemented as straightforward RAG.
Do you work with specific tools and models?
The choice depends on integrations, data requirements, latency, cost, and portability. We avoid unnecessary model lock-in when a stable interface is enough.
How do you handle high-risk actions?
We apply least privilege, argument validation, isolated environments, action limits, and human approval before irreversible operations.
Have a candidate task?
Describe the current workflow, systems involved, volume, exceptions, and impact of a wrong decision. We will assess whether an agent is the right solution.