AI agents built for real workflows. Tool use. Memory. Multi-step reasoning. Real orchestration.
Operonn builds agentic AI systems where the model is not just answering — it is acting. Tool calling against your internal systems, structured memory across turns, multi-step reasoning with error recovery, and a human handoff path where the stakes demand it. Production orchestration with evals, tracing, and monitoring baked in.
An agent is a loop, not a prompt.
The interesting AI work right now is not inside a single prompt — it is inside the loop that runs around it. Plan, call a tool, read the result, update state, decide the next step, recover from failure, know when to stop. That is an agent. Done well, agents replace brittle if-then pipelines that used to take a small team of integration engineers to maintain. The difference between a reliable agent and an unpredictable one is orchestration engineering — not model choice.
- Tool calling against your internal APIs, databases, and SaaS systems.
- Structured memory: short-term turn state plus long-term user and task memory.
- Multi-step reasoning with error recovery and explicit stop conditions.
- Confidence gating and human handoff where accuracy matters more than throughput.
Agents replace glue code, not people.
The highest-leverage place to deploy an agent is usually a workflow that currently requires a human to move information between three or four systems. Take a ticket, check a customer record, look up a policy, apply a rule, draft a reply, log the outcome. That workflow is an ideal agent — structured handoffs between tools, measurable outcomes, and a clear escalation path when the agent is not confident. We scope agents around these specific workflows, designed for measurable outcomes.
Orchestrate first. Prompt last.
A real agent build starts with the state machine, not the prompt. We model the tool surface, the allowed transitions, the memory contract, and the failure modes. Then we write the orchestration code with tracing at every step. Only then do we tune the prompts. This sounds boring — it is the reason our agents do not need to be babysat in production. Every agent ships with an eval harness that replays real traces, an automated grader for outcome correctness, and a judge model pinned separately from the system model.
- State machine design before prompt engineering.
- Explicit tool schemas with validated inputs and outputs.
- Full-trace observability — every tool call, every decision, every token.
- Eval harness with replayed traces and outcome graders.
The agent knows when to stop.
Guardrails are not a post-launch bolt-on. Every agent we ship includes confidence gating on critical actions, explicit authorisation checks before destructive operations, structured escalation to a human reviewer, and logging that makes the decision chain auditable after the fact. The goal is not a fully autonomous system — it is a system that is honest about the edges of its competence.
- Confidence gating on irreversible or high-impact actions.
- Explicit auth checks before writes, payments, or external messages.
- Structured escalation paths with full context handoff to a human.
- Auditable decision logs for compliance review.
Chosen for the problem. Not for the vendor.
MODELS
- Claude (tool use)
- GPT (function calling)
- Open-source with native tool schemas
ORCHESTRATION
- LangChain / LangGraph
- Custom state machines
- Temporal
- Durable executions
MEMORY
- Short-term context
- Long-term vector memory
- Structured profile stores
OBSERVABILITY
- OpenTelemetry
- Langfuse
- Custom trace UI
- Sampled trace review
Common questions.
What is AI agent development?
AI agent development is the engineering of systems where an LLM is the decision-making core of a loop that plans, calls tools, reads results, updates state, and decides next steps. It is distinct from a single-prompt chatbot — agents take action, maintain memory, and recover from errors.
Which agent framework do you use?
LangChain / LangGraph is our default for faster-moving engagements. For latency-critical or compliance-heavy systems we often write custom orchestration in Python or TypeScript. We pick per problem — framework choice is a cost, not a decision dimension on its own.
How do you handle agent hallucinations or bad tool calls?
Every agent we ship includes validated tool schemas, confidence gating on high-impact actions, an eval harness that replays real traces, and a judge model pinned separately from the system model. Bad tool calls become a testable regression — not an untraceable production incident.
Can the agent run on our infrastructure?
Yes. Agents run inside your cloud account, your VPC, or in a region that meets your data-residency constraint. You own the code and the model credentials.
How long does a first agent take to ship?
Most first agent builds ship in 6–10 weeks. State machine in week 1. Working tool calls and eval harness in week 3. Production-ready with handoffs and tracing by week 8.
Describe your workflow. We'll tell you if we can move the number.
Hi thereWhat can we help you with?
Have a workflow that needs an agent — not a chatbot?
Describe the state transitions and the tools. We'll tell you if an agent is the right shape.
hello@operonn.com →