Agents perceive, plan, and act.
Autonomous AI systems with structured reasoning, tool orchestration, and safety guardrails. From single-agent loops to multi-agent collaboration.
Seven stages of autonomous reasoning
Every agent follows this loop. Click any stage for technical detail.
Signal Perception
Receive and parse incoming signals — text, audio, API events, sensor data. Extract intent, entities, and urgency.
The agent observes its environment through structured signal intake. Every input is parsed for intent, entities, and context. Ambiguous signals trigger clarification sub-routines instead of guessing.
Four agent patterns we deploy
Different tasks need different reasoning architectures. We select the pattern that fits the complexity.
ReAct
Reason + Act in alternating steps
The agent alternates between reasoning (thinking about what to do) and acting (invoking tools). Each observation informs the next reasoning step. Best for tasks requiring dynamic adaptation.
Customer support, research assistants, troubleshooting
Specialized agents that collaborate
Complex tasks are decomposed across specialized agents, each with distinct capabilities and bounded responsibilities.
Supervisor
Routes tasks to specialists, manages workflow state, handles escalations.
Researcher
Retrieves knowledge from databases, documents, and APIs. Summarizes findings.
Analyst
Processes data, runs calculations, generates insights and visualizations.
Writer
Synthesizes findings into reports, emails, and presentations.
Executor
Invokes external tools, APIs, and systems to carry out approved actions.
Validator
Checks outputs for accuracy, compliance, and quality before delivery.
Real-world agent workflows
Step-by-step traces of agents solving real tasks in production.
Customer Inquiry Resolution
Parse customer email, extract intent: "billing dispute"
Pull account history, recent invoices, support tickets
Compare charges against contract terms, identify discrepancy
Generate resolution: credit $47.50, update account, draft response
Policy check: credit amount within auto-approve threshold
Apply credit, send response email, log resolution
87% resolved without human intervention. Avg 2.3 min vs 45 min manual.
Tool orchestration layer
Agents don't just think — they act. A typed tool registry with permission boundaries and sandboxed execution.
Data Access
Computation
Communication
System Actions
Every tool call → trace ID → inputs/outputs logged → policy gate → sandbox execution → result validation
Production metrics
Performance from real agent deployments. Measured, not estimated.
Task Success Rate
Tasks completed without human escalation
Avg Resolution Time
From signal to completed action
Tool Call Accuracy
Correct tool selected on first attempt
Safety Violations
Actions requiring post-hoc correction
Cost per Task
Average inference + tool cost
Human Escalation
Tasks requiring human decision
Five safety layers. Zero shortcuts.
Autonomous doesn't mean uncontrolled. Every agent operates within strict safety boundaries.
Input Filtering
Prompt injection detection, PII redaction, intent validation. Malicious inputs are blocked before reaching the agent core.
Permission Boundaries
Every tool has explicit permission scopes. Agents cannot escalate their own privileges. High-risk actions require human approval.
Output Verification
Hallucination detection, factual consistency checks, policy compliance validation. Failed checks trigger re-generation or human review.
Audit Trail
Every decision, tool invocation, and output is logged with trace IDs. Full replay capability for any agent session.
Circuit Breakers
Cost limits, execution time caps, and iteration limits prevent runaway agents. Automatic shutdown with human notification.
Go deeper
Explore the technology and architecture behind agentic systems.
Describe the tasks you want agents to handle.
We'll design the agent architecture, tool integrations, and safety boundaries.