Explicit permission boundaries
Each agent gets the narrowest scope of access that lets it do its job — nothing broader by default.
Autonomous Software Engineering
Agents that act, not just answer.
Most software waits for input. We build agents that plan, execute and integrate directly with your existing systems — with clear boundaries on what they're allowed to do.
Built on
Anthropic
Cohere
From "chat" to "action".
Standard LLM
Isolated (chat box)
Read-only mode
Waits for prompts
No memory
VS
SISI agent
Integrated (APIs, databases)
Read/write access, scoped deliberately
Proactive triggers
Long-term memory
How we keep it accountable
More capability means more guardrails, not fewer.
Giving a system read/write access to real tools is a real decision. Every agent we build carries these by default, not as an add-on.
Human checkpoints on real consequences
Anything with a financial, legal or customer-facing outcome routes through a person before it's final.
Full audit trail
Every decision and action an agent takes is logged and reviewable, not a black box.
Rollback built in
Actions an agent takes can be traced back and reversed — this isn't a one-way door.
Industry applications
Agents in action
Retail agents
Orchestrate inventory, support and sales across channels.
- Inventory sync: monitors stock levels and triggers reorder APIs when low.
- Shopper assistant: personalized guidance based on purchase history (CRM lookups).
- Returns automation: validates return policies and issues shipping labels instantly.
Medical agents
Reduce administrative burden around scheduling and follow-up — always with a clinician in the loop for anything clinical.
- Intake & scheduling: structures patient-provided information and books slots in the EHR system, with a clinician reviewing before any triage decision.
- Post-op follow-up: automated check-ins to monitor recovery progress, escalating anything unusual to a person.
- Insurance verification: real-time API calls to verify coverage before appointments.
Financial agents
Secure analysis and transaction monitoring — advisory outputs stay a recommendation for a licensed professional to act on.
- Fraud alerting: monitors stream data for anomalies and flags accounts for review.
- Reporting analyst: queries internal databases to generate recurring reports for leadership.
- Portfolio signal surfacing: tracks signals and prices to surface rebalancing considerations for a qualified advisor to evaluate, not to execute automatically.
Compliance agents
Automate rigorous document checks and audit preparation.
- KYC automation: validates ID documents against sanction lists.
- Contract review: highlights risky clauses in legal documents before human review.
- Audit trails: logs every agent decision for full traceability.
Want to see one working?
We can walk through a live agent connected to real systems (Stripe, HubSpot, Jira) on a short call — more useful than a canned demo video.
Engineering stack
Our tooling
Illustrative, not fixed — the agent tooling landscape moves fast, and we choose the right tool for the job rather than a single stack for every build.
Orchestration
LangChainFramework
LangGraphState
HaystackPipeline
Runtime
AutoGen (MS)Multi-agent
CrewAITask force
Assistants APIOpenAI
Memory
PineconeVector DB
QdrantSelf-host
RedisCache
Best fit
Agents work best when the workflow is real, bounded and worth governing properly.
Strong fit
- The workflow is repeated and well understoodA clear, recurring process is far easier to automate safely than a vague ambition.
- Someone owns the outcomeA named person reviews what the agent does and can adjust its boundaries.
- You already have foundational AI automation in placeAgents extend a working automation and data foundation — see AI Automation if that's not there yet.
Not the first move
- The goal is a one-off demoWe're most useful when an agent needs to survive daily use, not just impress once.
- No one has reviewed the access it would needThat review is part of the engagement, not a prerequisite — but it has to happen before launch.
- The workflow itself is still undefinedAn agent will only automate the confusion — Discovery, PoC & MVP is a better starting point.
See it in practice
Related delivery work
Next step