Build AI agents that take action. We work with LangGraph, CrewAI, Strands, and other frameworks to create agents that complete real work across your systems.
AI agents have evolved from experimental demos to production systems that work for hours on complex tasks. With GPT-5 and Claude maintaining focus across extended sessions, and frameworks providing robust orchestration, agents can now handle real enterprise workflows. We work with leading frameworks to build agents that deliver.
Today's frontier models enable agents that:
Extended autonomy. Work for longer sessions on multi-step tasks without losing the thread.
Coherent context. Maintain state and intent across long-running workflows.
Tool use. Call APIs, use browsers, and interact with application interfaces where needed.
Coordination. Combine planning, retrieval, and action execution into a single workflow.
Escalation. Know when to ask for help or trigger a human approval step.
Self-correction. Detect when an approach is failing and try alternatives.
This represents a step change from earlier generations where agents struggled with extended tasks.
Different frameworks suit different requirements:
LangGraph provides the most sophisticated orchestration for complex stateful applications. Its graph-based approach handles multi-step workflows with branching, cycles, and human-in-the-loop patterns. The go-to choice for serious agentic applications.
LangChain remains valuable for chains and simpler agent patterns. Good documentation, active development, and broad tool integrations.
CrewAI focuses on multi-agent systems where different agents handle different roles. Strong for scenarios requiring diverse expertise or parallel work streams.
AWS Strands provides production-grade agent infrastructure within the AWS ecosystem. Integrates with Bedrock AgentCore for enterprise deployment.
Semantic Kernel is Microsoft's framework, integrating well with Azure services. Suited for organisations deep in Microsoft's ecosystem.
Custom implementations sometimes make more sense than frameworks. When requirements are specific or frameworks add unnecessary complexity, we build directly on model APIs.
Agent-based approaches work well for:
Extended work sessions: Tasks requiring sustained effort over hours, not minutes. Complex code migrations, research projects, document processing.
Multi-system coordination: Workflows spanning CRM, email, databases, web applications, and other tools.
Variable paths: Tasks where the right approach depends on what is discovered along the way.
Browser-based work: Automating tasks that require interacting with web applications.
Research and analysis: Gathering information from multiple sources, synthesising findings, producing deliverables.
Agents are overkill for simple question-answering or tasks that follow fixed scripts.
Production agents require engineering discipline:
Defined tool sets: Clear specification of what tools agents can use and appropriate boundaries.
Guardrails: Constraints on agent behaviour to prevent undesirable actions.
Observability: Detailed logging to understand what agents are doing and why.
Checkpoints: Saving state so extended tasks can be interrupted and resumed.
Error handling: Graceful recovery when tools fail or approaches are not working.
Human oversight: Appropriate escalation and approval points for consequential actions.
Testing: Verification that agents behave correctly across diverse scenarios.
Clever prompts are not enough. Agents need proper engineering.
Agents typically need access to external systems:
Tool integration: Connecting agents to APIs, databases, and business applications.
Memory systems: Storing context and history for ongoing tasks.
Retrieval augmentation: Providing agents with relevant information from knowledge bases.
Computer use: Enabling agents to interact with web browsers and application interfaces.
Human-in-the-loop: Escalation and approval paths for decisions requiring human judgement.
We design and build these integrations as part of agent development.
We help organisations build agents that actually work in production:
Use case evaluation: Identifying where agents add value versus simpler approaches.
Framework selection: Choosing appropriate tools for your requirements.
Architecture design: Structuring agent systems for reliability and maintainability.
Development: Building with proper engineering practices and testing.
Deployment: Running agents with appropriate monitoring, controls, and operations.
Use these prompts to clarify whether an agent approach is appropriate and what it would take to deliver safely.
“Which parts of this workflow should be automated by an agent, and which parts should remain human-led?”
“What tools, data access, and guardrails would the agent need to complete the task safely?”
“What evaluation plan would prove the agent is reliable: success metrics, failure modes, and human review?”
A set of patterns and libraries for building AI systems that plan, use tools, maintain state, and complete multi-step work reliably.
No. For simple chat flows or single-step tasks, direct model calls can be enough. Frameworks add value when workflows have state, branching, retries, and tool orchestration.
We constrain tool access, add explicit approval points for consequential actions, and design fallbacks so the system fails safely when confidence is low.
Clear task boundaries, structured plans, state management, and observability. We also test with realistic scenarios and edge cases before rollout.
