Artificial Intelligence
Agentic AI Engineering
AI agents that plan, call tools and act within defined permissions, engineered with durable execution, observability and human approval at the points where it matters.
Overview
An agent is a model given goals, tools and the authority to take steps on its own. That autonomy is what makes agents useful for multi-step work such as research, triage and record updates, and it is also what makes them risky when they are built casually.
Our agentic engineering concentrates on the controls around the model: narrowly scoped tools, explicit state, durable workflows that survive failures, permission boundaries, and checkpoints where a person approves consequential actions. The result is software that behaves predictably enough to be trusted with real work.
Business applications
- 01
Case and ticket triage
Agents read incoming requests, gather context from CRM, billing and knowledge systems, classify urgency, draft a response or resolution plan, and route the case to the right team with a clear summary.
- 02
Research and analysis assistants
Agents search internal repositories and approved external sources, compare findings and assemble structured briefs with citations, leaving judgment and final conclusions to the analyst who requested the work.
- 03
Back-office task execution
Agents complete routine operations such as reconciling records, updating systems of record, requesting missing information by email and preparing documents for approval, with every action logged and reversible where possible.
- 04
IT and engineering operations
Agents investigate alerts, query logs and metrics, propose remediation steps or code changes, and open tickets or pull requests for engineers to review rather than changing production systems directly.
Engineering approach
- 01
Scope autonomy deliberately
Decide which actions the agent may take alone, which require approval and which are out of bounds, based on reversibility, cost of error and the regulatory context of the task.
- 02
Design tools and contracts
Expose capabilities through narrow, documented tools or Model Context Protocol servers with typed inputs, validation, least-privilege credentials and idempotent behavior wherever the underlying system allows it.
- 03
Engineer state and durability
Model the agent as an explicit graph or durable workflow, so long-running tasks can pause for approval, retry failed steps and resume without repeating side effects.
- 04
Evaluate trajectories, not only answers
Test complete sequences of reasoning and tool calls against scenario suites, including adversarial inputs and prompt injection attempts, and score whether the agent took appropriate steps along the way.
- 05
Deploy with oversight
Release gradually with tracing of every step, spend and rate limits, approval queues, kill switches and clear escalation paths to the people accountable for the outcome.
Technologies and platforms
Agent frameworks
- LangGraph
- OpenAI Agents SDK
- Claude Agent SDK
- Google Agent Development Kit
- Semantic Kernel
- LlamaIndex
Tools and integration
- Model Context Protocol
- OpenAPI
- REST and GraphQL APIs
- OAuth 2.0
Durable execution and state
- Temporal
- AWS Step Functions
- Apache Kafka
- PostgreSQL
- Redis
Observability and safety
- OpenTelemetry
- Langfuse
- LangSmith
- Arize Phoenix
- NVIDIA NeMo Guardrails
Technology names are trademarks of their respective owners and do not imply partnership or endorsement.
What you receive
- Agent design specifying goals, tools, permissions and approval points
- Tool layer or MCP servers connected to your systems of record
- Durable agent runtime deployed in your environment
- Scenario-based evaluation suite including security test cases
- Tracing, cost monitoring and human approval interfaces
- Operating procedures for escalation, rollback and incident response
Questions leaders ask
How do you stop an agent from doing something harmful?
By limiting what it can do in the first place. Agents receive only the tools and permissions their task requires, consequential actions pass through human approval, inputs are screened for prompt injection, and spend and rate limits cap runaway behavior. Every step is traced, so any unexpected action can be investigated and corrected.
What is the difference between an agent and workflow automation?
A workflow follows a path defined in advance; an agent chooses its next step based on what it observes. In practice, the most reliable systems combine both: deterministic workflows for the known path, with agentic reasoning reserved for the parts that genuinely require judgment or flexible handling of unstructured input.
Which agent framework do you use?
We select frameworks per project. LangGraph, the OpenAI Agents SDK, the Claude Agent SDK and Temporal-based workflows each suit different needs around state management, provider coupling and durability. We favor designs where business logic and tool definitions stay portable, so the framework can change without rewriting the integration layer.
Related solutions
Enterprise AI Transformation
Move AI from scattered pilots to governed systems that run the business.
Workflow and Process Automation
Automate the work between systems, and keep people in charge of what matters.
Intelligent Sales and Customer Operations
Give every seller and service agent the context to take the next right step.
Related industries
Discuss your Agentic AI Engineering initiative
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