What this solves
08
Autonomous agents that work through problems, not just respond to prompts.
When the bottleneck is knowledge work — research, analysis, multi-step decision chains — simple automation doesn't cut it. Agents do.
Get a quote Typical timeline: 4–10 wks
Process
How it works
01
Discover & Map
We audit your current processes and identify the highest-impact automation opportunities.
02
Design & Build
AI agents are LLM-powered systems that can use tools, maintain memory across sessions, plan multi-step action sequences, and hand off to other agents.
03
Monitor & Optimise
Your automation ships with monitoring, alerting, and a handover pack. We stay available for optimisation.
What's included
AI agents are LLM-powered systems that can use tools, maintain memory across sessions, plan multi-step action sequences, and hand off to other agents. We build production-grade agents using LangChain, LangGraph, and AutoGEN — integrated with your internal data via RAG pipelines, wired to your existing APIs, and deployed with the observability you need to trust what they're doing. Use cases include competitive intelligence agents, contract review agents, data analysis agents, sales research agents, and multi-agent coordination systems that replace entire manual workflows.
What you receive
- Agent architecture document — memory type, tool registry, planning strategy, and escalation logic
- Production agent codebase in Python (LangGraph or AutoGEN) with full test suite
- RAG pipeline connecting the agent to your internal knowledge base (Pinecone, Weaviate, or pgvector)
- Tool integrations — custom function calling wrappers for your APIs, databases, and third-party services
- Observability setup: LangSmith or Langfuse tracing so you can inspect every step of every agent run
- Evaluation harness with benchmark tasks and pass/fail criteria for regression testing after model updates
Typical outcomes
- Knowledge work tasks completed in minutes that previously took hours of analyst time
- Consistent, auditable reasoning traces for every agent decision — no black-box outputs
- Multi-agent pipelines where specialist agents (researcher, writer, critic) collaborate on complex deliverables
- Tool-use integration: agents that can query databases, call APIs, run code, and search internal knowledge bases
- Persistent memory so agents retain context across sessions and build institutional knowledge over time
- Human-in-the-loop checkpoints at configurable confidence thresholds so your team stays in control
Technology we use
Ready to discuss your project?
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Tools & integrations we work with
LangChainLangGraphAutoGENOpenAI GPT-4oAnthropic ClaudePineconepgvectorLangSmithPythonFastAPI
We integrate with your existing stack — no rip-and-replace required.
Questions
Common questions about AI Agent Development.
A chatbot responds to a single message. An agent receives a goal, breaks it into steps, decides which tools to use, executes them in sequence (or in parallel), evaluates the results, and iterates until the goal is met — all without you prompting each step. The difference in output quality and autonomy is significant.
We build agents with explicit tool permission scopes — an agent can only use the tools it's been given, and those tools only expose the minimum required access. We also add human approval gates for irreversible actions (sending emails, writing records, triggering payments). Every run produces a full reasoning trace you can audit.
It depends on the task. Claude 3.5 Sonnet has excellent instruction-following and is strong on document analysis. GPT-4o has a larger context window for complex multi-document tasks and strong code generation. We benchmark both on your specific use case during the discovery phase and pick the best fit — or use different models for different agent roles.
Yes. We can deploy agents using Azure OpenAI Service or AWS Bedrock, where your data stays within your cloud tenancy. Alternatively, we can fine-tune or use open-source models (Llama 3, Mistral) deployed on your own infrastructure with no data leaving the building.
A common pattern: a controller agent receives a task (e.g. "prepare a competitive analysis of these three companies"), spins up a researcher agent for each company in parallel, receives their outputs, passes them to a synthesis agent, and returns a structured report. Each specialist agent has narrow tools and a focused prompt — this produces better results than one agent trying to do everything.
A single-purpose agent (one goal, 3–5 tools, clear input/output contract) is typically production-ready in 4–6 weeks. Multi-agent systems with complex coordination take 7–10 weeks. The largest time investment is evaluation — rigorously testing the agent against edge cases before handing over.