Agents that plan, act and check their work
An agent is more than a prompt. It plans, calls tools, remembers context, and knows when to ask a person. We engineer these systems for production: observable, testable, and safe to let near real data.
Most agent demos break the first week in production
A single model call can look brilliant in a demo. In production the same system has to handle bad inputs, slow tools, partial failures, ambiguous requests and users who change their minds mid-task.
We treat an agent as a distributed system with a language model in it. That means explicit state, idempotent actions, evaluation before release, and a clear boundary between what the agent may do alone and what needs a human decision.
- Explicit control flow. Plans, handoffs and loops you can read, not hidden in a prompt.
- Safe actions. Tool calls are validated, idempotent and logged.
- Grounded answers. Retrieval over your data with sources attached.
- Measured quality. Evaluation sets and tracing before and after every change.
Six focus areas
Six disciplines make up an agent system. Each page covers how we approach it and what you receive.
Coordinating several agents without losing the plot
Supervisor-specialist, hierarchical or peer handoffs: the pattern should follow the problem.
Explore architecture & specsTool calling & API actionsLet agents act without double-charging anyone
Tool calls turn a language model into software that does things.
Explore architecture & specsHybrid RAG on PostgreSQLAnswers grounded in your data, with sources
Hybrid retrieval combines vector search, keyword search and structured filters so answers come from the right documents.
Explore architecture & specsMemory & knowledge graphsMemory that helps, not memory that drifts
Agents need short-term context, durable facts and a way to forget.
Explore architecture & specsHuman-in-the-loop workflowsPut people exactly where judgement matters
Full autonomy is rarely the goal.
Explore architecture & specsEvaluation & observabilityKnow it works before and after every change
Prompts and models change behaviour.
Explore architecture & specsHow an engagement runs
Frame the job
Define the task, the success measure and the actions the agent may take.
Design the architecture
Choose the orchestration pattern, tools, memory and review lanes.
Build and integrate
Implement the agents, tools and data access against your systems.
Evaluate
Run an evaluation set, trace failures and tune.
Ship and observe
Release gradually with monitoring, cost tracking and a feedback loop.
Technology we use for this work
Engineering notes on this topic
Supervisor vs hierarchical agent orchestration: how to choose
Three orchestration patterns, what each costs you, and a simple rule for picking the smallest one that works.
Read the insightAgentic AIHybrid RAG on PostgreSQL: vectors, keywords and filters together
Why vector search alone fails on real enterprise data, and how to combine pgvector, full-text search and metadata in one database.
Read the insightAgentic AIDesigning human-in-the-loop review lanes for AI agents
Autonomy is a dial, not a switch. How to decide what an agent may do alone, what needs a person, and how to build review that is fast.
Read the insightQuestions we hear
What is the difference between a chatbot and an agent?
A chatbot answers. An agent decides what to do next: it can plan multi-step work, call tools and APIs, read and write data, and loop until a goal is met or it needs a person.
Which models do you use?
We choose per task from leading providers such as OpenAI, Google and Anthropic, and design the system so models can be swapped as the field changes.
How do you keep agents from doing damage?
Actions go through validated tool interfaces with least-privilege access, idempotency keys and audit logs, and sensitive steps are routed to a human for approval.
Can you work with our existing stack?
Yes. We integrate with your databases, APIs and identity provider, and deploy to your cloud account.
How do we start?
A technical discovery call to define the use case, the data and the risks, followed by a scoped plan.
Scope your agent system with an engineer
Bring the process you want to automate. We will talk through the architecture, the risks and a realistic first release.