Agent Frameworks
Build AI agents that take action. We use LangGraph, CrewAI, Strands and other frameworks to build agents that plan, use tools and complete real work.
Agent framework landscape
Different agent frameworks suit different requirements. We select the right tool for each project based on complexity, control and deployment needs.
LangGraph
Graph-based agent orchestration with explicit state management, human-in-the-loop support and persistence. Strong for complex, multi-step workflows.
CrewAI
Role-based multi-agent collaboration where specialised agents work together on tasks, each with defined responsibilities and tools.
Strands and others
Lightweight agent libraries and emerging frameworks for simpler use cases where full orchestration overhead is unnecessary.
What agent frameworks enable
Agent frameworks provide the structure for AI to move from answering questions to completing tasks.
Tool use
Agents that call APIs, query databases, run code and interact with external systems to gather information and take action.
Planning
Breaking complex tasks into steps, reasoning about dependencies and executing plans with appropriate error handling.
State management
Tracking progress through multi-step workflows with persistence, checkpoints and the ability to resume interrupted tasks.
Human-in-the-loop
Pause-and-approve patterns that keep humans in control for high-stakes decisions while automating routine steps.
Multi-agent collaboration
Multiple specialised agents working together, each handling a different aspect of a complex task or domain.
Observability
Tracing, logging and monitoring of agent behaviour so you can understand what your agents did, why, and where they went wrong.
Use cases for agent frameworks
Research automation
Agents that gather, synthesise and report on information from multiple sources with citations and structured output.
Data pipelines
Intelligent data processing that adapts to varied inputs, handles edge cases and escalates when confidence is low.
Customer operations
Agents that resolve customer issues by querying systems, applying business logic and taking action across multiple tools.
Code review and testing
Automated code analysis, test generation and review workflows that integrate with development toolchains.
Document workflows
Processing, classifying and routing documents through multi-step approval and enrichment workflows.
Sales and outreach
Research prospects, personalise communications and manage follow-up sequences with human oversight.
How we deploy agents
Agent deployment requires thought about reliability, monitoring and cost control that goes beyond the framework itself.
LangGraph Cloud
Managed deployment for LangGraph agents with built-in persistence, streaming and human-in-the-loop.
Self-hosted
Deploy agent frameworks on your own infrastructure with full control over data, compute and networking.
Serverless
Event-driven agent execution on Lambda, Cloud Functions or similar platforms for cost-efficient scaling.
Frequently Asked Questions
Do we need an agent framework, or can we just use the API directly?
For simple tasks, direct API calls are fine. Frameworks add value when you need multi-step execution, tool use, state management or human-in-the-loop controls.
Which framework should we choose?
It depends on your requirements. LangGraph suits complex stateful workflows, CrewAI works well for multi-agent collaboration, and lighter libraries suit simpler use cases.
How do you prevent agents from going off track?
Through constrained tool access, well-defined state machines, output validation, cost limits and human approval gates for high-stakes actions.
What about agent costs running out of control?
We implement token budgets, step limits and monitoring. Architecture-level decisions about when to use agents versus simpler approaches keep costs predictable.
Build AI agents that work
We help organisations build agents that complete real work reliably. Book a call to discuss your agent development needs.