Conversational
A customer message comes in.
The agent replies in real time over the SDK, streaming the reply, tool calls, and status as typed events. The surface your customers talk to for support, sales, and service.
Agents that harness your business' IP.
The people who know the work shape the agent in plain language, and change it without an engineering queue.
In production, governed turn by turn
Action Request
Prompts
Policies
Routines
Glossary
Macros
Variables
Tools
Knowledge Base
Dynamic Context Engineering
Response Generation
Customer Reply
Dynamic Context Engineering
Prompts
Policies
Routines
Glossary
Macros
Variables
Tools
Knowledge Base
Until now, every AI framework forced a trade-off between control and adaptability.
Control
Adaptability
Workflow-driven frameworks
Rigid routingSiloed branchesOutput uncontrolled
Pre-defined node sequences force rigid flows, so any topic switch or compound question breaks them, and output inside a node stays uncontrolled.
Skills-based approaches
Modular, ungovernedNo execution engineNo attribution
Skills load when the model deems them relevant, with no engine ruling which apply and no way to trace an output back to a rule.
System prompt agents
Instruction fatigueUnbounded failuresSurface exposed
Every rule loads into one monolithic prompt, so as they pile up the model can't weigh competing priorities and reliability degrades.
Workflow-driven frameworks
Rigid routingSiloed branchesOutput uncontrolled
Pre-defined node sequences force rigid flows, so any topic switch or compound question breaks them, and output inside a node stays uncontrolled.
Skills-based approaches
Modular, ungovernedNo execution engineNo attribution
Skills load when the model deems them relevant, with no engine ruling which apply and no way to trace an output back to a rule.
System prompt agents
Instruction fatigueUnbounded failuresSurface exposed
Every rule loads into one monolithic prompt, so as they pile up the model can't weigh competing priorities and reliability degrades.
ControlAdaptability
What makes Interactive Agents different?
Rigour without rigidity.
Structured by composable parts
Policies, routines, tools: your operational logic, made explicit for developers, domain experts and coding agents. No more prompt slurp.
Policies
Routines
Tools
Glossary
Variables
Prompts
Macros
Knowledge Base
Interpretable by design
Every decision, tool call, and action traceable and replayable, so you see not just what the agent did but why.
trace · turn #422.3s
- p_142policyrefund_eligibility↳ matched: cap_exceeded
- t_018toolget_customer_tier↳ resolved: gold · priority
- #a7f2actionapply_$500_cap↳ audit: replayable
Dynamic Context Engineering
Combine the adaptability of LLMs with algorithmic determinism to ensure your agents always see what they need to, when they need to.
policy library47 policies
3 of 47 policies matched this turn
refund_eligibility
customer_tier_var
compound_topic_h
AI Agents you can trust. Enforcing your rules, on every interaction.
Tighter context. A better agent, for less.
It only sees what it needs
Every turn, the agent sees the policies, routines, and memory that turn needs, nothing more. No giant prompt, no context bloat.
10 to 100x cheaper
Because the context stays tight, you spend a fraction of the tokens other agent frameworks burn on every call.
Smaller models hold up
With tight context and explicit steps, smaller, cheaper models match the big ones, with no accuracy tax.
Robust on long, complex work
The agent runs one step at a time, and backtracks when a step goes wrong, so long, multi-step processes stay on the rails.
Validates before it executes
At startup, an evaluation pass rewrites and checks your instructions, so there are no semantic gaps and prompts are optimised for the model. It is the CAPEX that buys a lean, reliable runtime.
Structured, not a prompt dump
Your information, rules, and processes as structured, versioned context. One framework for every business process, never an ungoverned system prompt.
Dynamic context engineering
Structured and versioned context, dynamically assembled.
Explore Dynamic Context Engineering
Trace
Every prompt, decision, and tool call captured in one trace, so you can see which policy fired.
Evaluate
On the Platform's Improvement layer, evaluators score every interaction against your quality bar, so regressions surface the moment they appear.
Compound
Traces, scores, and operator annotations feed back into policies, routines, and memory. The agent compounds, week over week.
Every interactionmakes your agent smarter.
The agent you ship on day one is the weakest it will ever be. In production, it only gets stronger.
Why we builtInteractive Agents?
Most enterprise AI projects fail in the handoff between the team that knows the rule and the team that can ship it.
Interactive Agents close that gap. Domain experts author behavior in plain language. Structured evaluation enforces it on every turn. Build, govern, operate, improve, in one environment that compounds with every interaction. AI becomes operational IP: structured, versioned, executable.
Conversational, or fully autonomous.
Autonomous
An event or webhook fires.
The agent runs routines end to end with no conversation: typed JSON in, typed JSON out to a signed callback, on the same governed runtime.
Author and ship
Build them the way your team works.
Describe it to the Copilot, scaffold it from the CLI, or commit the manifest to your repo: one versioned spec, reviewable like code.
InteractiveAI Copilot
Describe what you want. We'll scaffold the agent, components, policies, governance.
↵ to send · ⇧ ↵ for newline
Try
The Pilot
Run a 2 to 6 week Pilot to production.
Scoped use case · agent built by your domain experts · governance kit · traceability dashboard · post-Pilot expansion plan.