Agentic AI
AI agents that complete work, safely.
Move beyond chatbots. We design and build agents that plan, use your systems and APIs, and complete multi-step tasks, with the guardrails, approvals and audit trails enterprises need.
Why it matters
From answering questions to getting work done
Generative AI's first wave answered questions. Agentic AI completes tasks: reading a claim, checking policy coverage, requesting a missing document, drafting a decision and routing it for approval. The opportunity is large, and so is the risk of letting software act on your behalf.
We build agents the way you'd onboard a new employee: a clear job description, the minimum system access required, defined escalation paths, supervision proportional to risk, and performance reviews based on data.
Our agent design principles
- Bounded autonomy: autonomy levels set per action, from suggest-only to fully automated, based on risk and reversibility.
- Least privilege: scoped credentials, read-only by default, and every tool call logged.
- Deterministic where it matters: workflows and business rules for steps that must be predictable; LLM reasoning only where judgment adds value.
- Evaluated continuously: task success rate, accuracy, escalation rate, cost and latency, tracked per agent.
What we deliver
Capabilities
Delivered by senior architects and specialist engineers, with security and governance designed in.
Agent & workflow design
Task decomposition, autonomy levels, escalation rules and human approval design.
Tool & API integration
Secure connectors to CRM, ERP, ITSM, document systems and data platforms, including MCP-based tool access.
Memory & context
Retrieval, state management and context engineering so agents act on accurate, current information.
Guardrails & security
Prompt-injection defenses, policy checks, sandboxing, spending limits and full audit trails.
Evaluation harnesses
Scenario-based test suites, simulation and regression tests before every release.
Agent operations
Monitoring, cost controls, incident response and continuous improvement in production.
Use cases
Where it pays off
- Claims & case processingIntake, document checks, coverage lookups and decision drafts for adjusters.
- IT service managementTicket triage, diagnostics, runbook execution and resolution notes.
- Finance operationsInvoice matching, exception resolution, accrual support and close checklists.
- Customer operationsOrder changes, returns, account updates with policy checks and approvals.
- Sales operationsAccount research, CRM hygiene, quote preparation and follow-up drafting.
- ProcurementSupplier onboarding, contract clause review and spend analysis.
Approach
How the engagement runs
Clear phases, clear exit criteria, and a working result at every stage.
Assess
Value-sized use cases, data and platform readiness, risk assessment and a prioritized roadmap.
Prove
A working pilot on real data with an evaluation suite, cost per task and security review.
Productionize
Integrations, guardrails, observability, human-in-the-loop design and change management.
Scale & operate
Shared AI platform components, new use cases and managed operations with SLAs.
Typical starting engagement
Agentic AI Pilot
Fixed scope, fixed fee, and a clear decision at the end. Final pricing is confirmed after a short scoping call.
Scope this engagement →Starting at
- Process and risk analysis for one workflow
- Production-grade agent with tool integrations
- Guardrails, approvals and audit logging
- Evaluation suite and performance dashboard
- Go-live plan and operating runbook
FAQ
Frequently asked questions
Straight answers to the questions CIOs, CTOs and procurement ask us first.
What is agentic AI?
Agentic AI refers to AI systems that can plan and execute multi-step tasks by using tools: calling APIs, querying databases, updating records and coordinating with people or other agents, rather than only generating text in response to a prompt.
Are AI agents safe to use in regulated processes?
They can be, with the right design: bounded autonomy, least-privilege access, human approval for consequential actions, deterministic controls for rule-based steps, and complete audit logs. We design to your risk appetite and regulatory obligations.
Which agent frameworks do you use?
We select per use case among cloud-native agent services and open frameworks, and we design around open standards such as the Model Context Protocol for tool access, so you avoid lock-in.
How do you measure agent performance?
Task completion rate, accuracy against expected outcomes, escalation and override rates, cycle time, cost per task and user satisfaction, all baselined against the current process.
Start here
Get a senior architect's view of your fastest path to value.
A 45-minute working session, not a sales pitch. Bring your toughest AI, data or cloud problem; leave with a point of view, a rough plan and a realistic budget range.
- Senior architects onlyNo account managers in the first meeting.
- Fixed-fee optionsClear scope, price and outcomes up front.
- Mutual NDA on requestBefore any detailed discussion.