You want real AI agents.
Not ChatGPT wrappers.
We design and build the autonomous AI agents your enterprise needs. Real agents with total memory, structured reasoning and auditable governance — not Zapier automations dressed up as AI.
Automation follows rules.
An agent makes decisions.
Most "AI agents" sold today are Zapier or n8n workflows behind an LLM layer. Here's what sets a real autonomous agent apart.
| Aspect | Classic automation | Real AI agent |
|---|---|---|
| Behavior | Runs a chain of preprogrammed rules (if X then Y). | Observes, reasons, picks an action, learns from the result. |
| Memory | None. Every run is isolated. | Complete memory of past actions (immutable, replayable Event Store). |
| Scope | Defined line by line in the workflow. | Defined by architectural constraint: the agent can't step outside its bounded context. |
| Decision | Boolean on input values. | Confidence score + structured reasoning + alternatives considered. |
| Audit | Execution logs, which expire. | Each decision = immutable domain event, traceable 5 years later with dual attribution. |
Neurosymbolic architecture, the same one we use for our own agents.
An agent that doesn't know what it knows isn't reliable. Our agents combine an LLM (reasoning, language) with an explicit, verifiable representation of the business they operate in. Five cognitive representation layers.
- 01
Structural representation
DDD metamodel
Your agent's business (entities, rules, events) is described in a metamodel. The agent doesn't guess the structure, it reads it.
- 02
Temporal representation
Event Store
Every observation, action and decision is kept as a timestamped event. The agent has total memory, replayable identically.
- 03
Procedural representation
declarative rules
The business logic (permissions, triggers, conditions) is declared, hot-editable without recompilation.
- 04
Vector representation
specialized embeddings
For similarity retrieval (documents, past conversations, similar cases), used where relevant, bounded by the domain.
- 05
Organizational representation
executable Conway's Law
If multiple agents collaborate, their structure mirrors your organization's. Each agent is tied to a domain, never an autonomous decision-maker.
This architecture places Swoft in the Neuro | Symbolic (Type 3 Kautz) category: the LLM converts natural language into symbolic structures, which are then processed by a formal reasoner. It's the approach research identifies as the most mature for critical enterprise systems.
What we build for our customers.
Each agent is custom-built for the customer's business. The types we design most often, in multi-agent systems orchestrated by event-sourced sagas.
Compliance assistant
Reads new regulatory texts, identifies impacts on your processes, proposes actions, escalates what requires a human decision.
Scoring agent
Evaluates credit applications, candidates, prospects. Every decision is traced with its reasoning and remains replayable in front of a regulator.
Sales qualification
Triages incoming leads, enriches them, first contact, meeting scheduling. Scope framed to avoid missteps.
Tier 1 support
Answers tickets, escalates those outside its scope, learns from every conversation to improve its replies.
Document analysis
Structured extraction from contracts, invoices, reports. Cross-checked against your business metamodel for automatic validation.
Internal triage (HR, IT)
Classifies incoming requests, suggests answers, routes to the right person. Dual attribution on every action.
Six non-negotiable guarantees,
by construction, not by review.
An agent in production must be auditable, governable, and survive model changes. Here's what's in the DNA of every agent we deliver.
Decisions as Data
Every AI decision is an immutable event: reasoning, model, score, prompt, alternatives. Deterministic replay guaranteed at 5 years.
Conway-aligned personas
If your project involves multiple agents, their structure mirrors your organization's. Formal scope, never autonomous decision-makers.
Dual Attribution
Every action records who authorized (human) and who executed (agent). Complete audit trail by construction, not by manual review.
Architectural Hard Stop
The metamodel mechanically blocks any drift between intent and code. The agent technically cannot invent undefined behavior.
Configurable approval gates
On sensitive decisions, the agent automatically pauses and waits for your validation. You decide the confidence threshold per use case.
Model-agnostic
Claude, GPT, Mistral, Llama, local models: the architecture survives model changes. You don't depend on a vendor.
From brief to production in 4 to 8 weeks.
A Swoft engineer is the dedicated lead on the whole project. We commit to the functional scope, not to time and materials.
- 011 week
Framing
Business brief + domain mapping + identification of rules, events and decision points. Deliverable: executable specifications.
- 022 to 6 weeks
Design & development
Agent modeling in the metamodel, integration with existing systems, LLM calibration, approval gates setup.
- 031 week
Go-live
EU sovereign deployment, continuous monitoring, dedicated engineer who tracks decision quality and adjusts thresholds.
Your agents share the architecture of our own agents.
Swoft is built with Swoft. The AI agents that drive our own product lifecycle (modeling, code generation, quality audit, deployment) rest on exactly the same architecture as the ones we deliver to customers. What you see working at our place will work at yours — same properties, same guarantees, same rigor.
An AI agent to design?
30 minutes to frame it.
You describe the need, we tell you honestly whether an autonomous agent is the right answer, what it takes to build, and what it costs.