Agents that reason, act, and answer to you.
We engineer production AI agents and multi-agent systems that plan, use your tools, remember context and stop for a human when the stakes are high — with every step traced.
Provision a high-availability Kubernetes cluster, configure an Istio service mesh, and deploy 14 microservices with zero downtime.
- PlannerTask decomposition & DAG
- ResearcherCloud topology & quota analysis
- ExecutorTerraform & Helm dispatcher
- ValidatorCanary health & chaos probes
- $ jp-swarm run --objective "Autonomous cloud swarm"
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The brain behind every agent we ship.
Every request takes the same governed path: screened, grounded in your context, planned by a supervising model, executed by scoped specialists and validated before it leaves the system.
- [01]
Guardrail check
Input is screened for prompt injection, PII and policy scope before any model sees it.
- [02]
Context assembly
Conversation state, your SOPs and role permissions are compiled into a bounded working context.
- [03]
LLM planner
A supervising model decomposes the goal into a plan and picks the next best action.
- [04]
Tools & memory
Specialist steps retrieve knowledge, call APIs or hand off to a human — each under its own rules.
- [05]
Validation
Outputs are checked against sources and policy; only verified actions leave the system.
Four parts. Each engineered, not prompted.
Reliability comes from architecture, not clever prompts. These are the components we build, test and monitor independently.
- [01]
Planning
ReAct and plan-and-solve loops decompose goals into steps, then replan when a tool fails or the world changes.
ReAct · Plan-and-Solve · Reflexion
- [02]
Memory
Short-term working context in Redis, long-term episodic and semantic memory in vector and graph stores.
Qdrant · Pinecone · Neo4j · Redis
- [03]
Tools
Typed, permission-scoped tool calls: SQL, internal APIs, browsers via Playwright and sandboxed code execution.
Function calling · MCP · Playwright
- [04]
Guardrails
Input and output validation, PII masking, prompt-injection shields and policy checks on every action.
OWASP LLM Top 10 · PII redaction
One agent, or a team of them?
Most problems need one well-built agent. Some need specialists that check each other's work. We'll tell you which — and design for the simplest thing that meets the bar.
Single agent
Start here- Best for
- Well-bounded tasks with one owner
- Latency
- Lowest — one reasoning loop
- Cost per task
- Predictable, small
- Reliability
- Guardrails + self-check
- Build effort
- 2–4 weeks to production
Multi-agent system
Scale here- Best for
- Cross-domain work that needs specialists
- Latency
- Higher — parallel branches, consensus step
- Cost per task
- Higher, offset by throughput
- Reliability
- Peer review and validator consensus
- Build effort
- 6–10 weeks to production
Multi-agent topologies
Hierarchical manager–worker
An orchestrator decomposes goals into a task graph, assigns specialists, monitors execution and handles retries.
Best for Software engineering, research, financial audit
Peer consensus mesh
Specialists challenge each other's assumptions and vote before any action is taken, cancelling individual hallucinations.
Best for Clinical triage, fraud prevention, contract review
Sequential assembly line
A strictly ordered, stateful pipeline where each agent enriches the payload behind a deterministic validation gate.
Best for ETL, document processing, compliance checks
Dynamic router
A classifier scores each request's complexity and dispatches it to a lightweight agent or a heavyweight swarm.
Best for Support copilots, high-volume transactional APIs
Where agents earn their keep.
Autonomy where it's safe. You where it matters.
Agents pause on high-stakes actions and hand you everything needed to decide in seconds. Try it — approve or reject the refund.
- Policy-driven checkpoints
- Thresholds on money, data mutations and outbound messages pause the agent automatically.
- Full reasoning trace
- Reviewers see the plan, sources and tool calls — not just the final answer.
- Signed audit trail
- Every approval and override is logged with identity for SOC 2 and internal audit.
- Learning from overrides
- Rejections feed evaluation sets so the agent asks less often over time.
Refund $4,820.00 to account ACME-2231
- Matched invoice INV-88213 to duplicate charge on 14 Oct
- Verified customer tier: Enterprise · contract allows full refund
- Drafted ledger reversal and customer notification
policy.match refunds > $2,500 require finance sign-off
Agent questions, answered.
Chatbots merely generate text replies to prompts. Autonomous agents perceive objectives, create step-by-step action plans, execute code/APIs, verify results, and iteratively solve problems until the goal is achieved.
Put your first agent into production.
Bring one workflow. In a 45-minute session we'll map it to an agent architecture, the guardrails it needs and a realistic path to production.