Autonomous Systems
AI Agents & Agentic Frameworks
Autonomous AI agents that reason, plan, and execute multi-step work — with the tool access, permissions, and oversight to be trusted in production.
Agents go beyond question-answering: they take actions. An agent can look up an order, draft a response, update a record, schedule a follow-up, and know when to stop and ask. Built well, agents absorb entire categories of repetitive knowledge work.
The hard part isn't getting an agent to act — it's getting it to act reliably and safely. That's harness engineering: the scaffolding around the model — structured outputs, tool contracts, retries, state management, context control, and permission boundaries — is what separates a demo that works once from a system that works every day. Most of our agent work goes into the harness, and that's deliberate.
We build on established agent design patterns — planner-executor for decomposing complex tasks, orchestrator-workers for parallelizing them across specialist agents, and evaluator-critic loops where one model checks another's work before it counts. We choose the simplest pattern that solves the problem, because in agent systems simplicity is what makes behavior predictable.
Capabilities
Tool-using agents
Agents that call your APIs, query your databases, and operate your internal systems through well-defined, permissioned tool interfaces.
Agent design patterns
Planner-executor, orchestrator-workers, evaluator-critic loops, and multi-agent systems — proven patterns matched to the task rather than a one-size framework.
Harness engineering
Robust scaffolding around the model: structured outputs, retries, state management, context control, and failure recovery that make agent behavior dependable.
Human-in-the-loop controls
Approval gates, review queues, and dry-run modes for actions that shouldn't happen without a person signing off.
Observability & audit trails
Full traces of what an agent saw, decided, and did, so behavior can be debugged, audited, and improved.
Evaluation & loop engineering
Scenario suites and eval-driven iteration: every change to prompts, tools, or models is measured against real tasks before it ships, and production runs feed the next round of improvements.
Typical use cases
- Back-office automation: intake processing, data entry, reconciliation, and follow-ups
- Research and enrichment agents that gather, verify, and summarize information
- Operations copilots that monitor systems and take approved corrective actions
- Email and ticket triage that classifies, drafts, and routes with human review
Our approach
- 1
We map the workflow first — every step, decision, system touched, and failure mode — before any agent code is written.
- 2
We define tool interfaces with explicit permissions and start agents in read-only or draft-only mode, expanding autonomy as measured reliability earns it.
- 3
We iterate in tight, eval-driven loops: every run is traced, success rates are tracked against a scenario suite, and the harness is tightened wherever real-world runs expose weaknesses.
Every engagement follows our four-phase process — discover, design, build, and optimize. See how we work.
Technologies we commonly use
FAQ
How do you keep an agent from doing something it shouldn't?
Layered controls: agents only get the specific tools they need, each tool enforces its own permissions, consequential actions require human approval, and every step is logged. Autonomy is expanded gradually, based on measured reliability.
What's the difference between a chatbot and an agent?
A chatbot answers; an agent acts. Agents plan multi-step work, call tools and APIs, and carry state across a task. Many systems we build combine both — a conversational front end with agentic execution behind it.
Do agents replace our team?
The systems we build absorb repetitive, well-defined work and route judgment calls to people. The goal is leverage for your team, with humans staying in control of anything consequential.
Do agents have to run on frontier model APIs?
No. Many agent steps — classification, extraction, routing, drafting — run well on smaller open-weight models like Llama or Mistral served with vLLM on your own GPUs, which keeps sensitive data in-house and cuts per-token costs at volume. We mix models deliberately: frontier models where reasoning quality demands it, self-hosted models where it doesn't.
Ready to talk about ai agents?
Tell us where you are and where you want to go. We'll come back with a candid read on what's possible and a concrete path to get there.