Racket: Programmable Programming for Constraint Natural Language of AI Agents

The Language of Languages

Racket isn’t just a programming language — it’s a language laboratory. Born from the Scheme tradition, Racket has evolved into a platform where creating new programming languages is as natural as writing functions. This unique capability makes it the perfect foundation for building Constraint Natural Languages (CNL): human-readable languages that express computational constraints, rules, and logic in near-natural prose.

For AI agents operating in complex environments, CNLs provide a bridge between human intentions and machine execution. Rather than forcing humans to think in terms of if-statements and loops, CNLs allow you to express agent behaviors, policies, and constraints in domain-specific terms that feel natural yet remain precise enough for computation.

Grammar Flexibility: The #lang Revolution

At the heart of Racket’s power is the #lang mechanism. Every Racket program begins with a line like #lang racket or #lang typed/racket, but this isn't just a version declaration—it's literally selecting which language you're programming in. Want to create your own language? Define a module that implements the language's semantics, and suddenly #lang my-language becomes a real, usable programming language.

This flexibility extends to syntax itself. Racket’s reader macros allow you to customize how text is parsed into syntax trees. You’re not limited to S-expressions (though they’re wonderfully uniform for metaprogramming). You can create languages that look like Python, Haskell, or even natural English.

Here’s a simple example of a CNL for agent rules:

#lang racket

(require (for-syntax racket/base syntax/parse))(define-syntax (agent-rule stx) (syntax-parse stx [(_ "when" condition "then" action) #'(λ (state) (when condition action))]));; Usage:(define move-to-target (agent-rule "when" (< (distance state 'target) 10) "then" (move-toward 'target)))

But we can go further. With custom reader syntax, we could make this even more natural:

#lang agent-cnl

when distance to target < 10 metersthen move toward target at speed 2 m/s

The #lang agent-cnl declaration tells Racket to use our custom language module to parse and interpret this text, transforming natural-looking constraints into executable code.

Extending Language with Libraries

In most languages, libraries provide functions and data structures. In Racket, libraries provide language features. When you (require racket/match), you're not just importing pattern matching functions—you're extending the language itself with pattern matching capabilities.

This compositional approach is transformative for CNL development. You can mix and match language features from different libraries, creating custom language blends tailored to your domain:

#lang racket

(require racket/match ; pattern matching racket/contract ; runtime contracts threading ; threading macros generic-bind) ; monadic operations;; Now we have a language that combines all these features

For AI agents, this means you can compose languages that combine:

Temporal logic (for time-based constraints)

Spatial reasoning (for location-based rules)

Resource management (for allocation constraints)

Communication protocols (for multi-agent coordination)

Each component is a library that extends the base language, and they compose cleanly because they all operate on the same underlying Racket foundation.

Optional Types: The Best of Both Worlds

Typed Racket brings gradual typing to the Racket ecosystem. This is particularly valuable for CNL development because it allows you to:

Prototype quickly with dynamic typing

Add types incrementally as requirements become clear

Guarantee correctness where it matters most

For AI agents, type safety can prevent entire classes of errors. Consider agent contracts:

#lang typed/racket

(: Agent-State (HashTable Symbol Real))(: Agent-Action (U 'move 'wait 'communicate))(: agent-decide (-> Agent-State Agent-Action))(define (agent-decide state) (cond [(< (hash-ref state 'energy) 0.2) 'wait] [(> (hash-ref state 'distance-to-goal) 5.0) 'move] [else 'communicate]))

The type system guarantees that agent-decide always returns a valid action, and that the state hash contains the expected keys with numeric values. These guarantees propagate through your entire CNL implementation.

Even better, you can mix typed and untyped code in the same project. Start with an untyped prototype CNL, then gradually add types to critical components as the design stabilizes.

Cur: Dependent Types and Proof-Carrying Code

Cur takes Racket’s type capabilities to the next level with dependent types — types that can depend on values. This enables you to encode complex invariants directly in the type system, creating CNLs where illegal programs are literally unrepresentable.

Dependent types are particularly powerful for AI agents because you can prove properties about agent behavior at compile time:

#lang cur

;; Define a type for battery levels (0-100)(define-type BatteryLevel (Σ ([level : Nat]) (and (<= 0 level) (<= level 100))));; An action that requires minimum battery(define-type (SafeAction (min-battery : Nat)) (Π ([current : BatteryLevel]) (-> (>= (fst current) min-battery) Action)));; This function MUST prove the battery level is sufficient(define/rec/match high-power-move : (SafeAction 50) [(current prf) ;; The proof 'prf' guarantees current >= 50 (execute-movement current)])

In this example, high-power-move cannot be called with a battery level below 50—not because of a runtime check, but because such a call would fail to type-check. The type system enforces the constraint.

For CNLs, this means you can create languages where domain constraints are enforced by the compiler. An agent coordination protocol could guarantee deadlock-freedom. A resource allocation DSL could prove that resources are never over-allocated. These aren’t runtime checks — they’re mathematical proofs embedded in the type system.

Rosette: Symbolic Execution and Constraint Solving

Rosette is a solver-aided programming language built on Racket. It allows you to write programs that contain symbolic values and constraints, then uses SMT (Satisfiability Modulo Theories) solvers to find concrete values that satisfy those constraints.

This is transformative for AI agent planning and verification:

#lang rosette

(define-symbolic* x y z integer?);; Define constraints for agent positioning(define constraints (and (>= x 0) (<= x 100) ; x position bounds (>= y 0) (<= y 100) ; y position bounds (= (+ (* x x) (* y y)) z) ; z is distance squared (< z 2500))) ; must be within 50 units;; Find a solution(define solution (solve constraints))(if (sat? solution) (evaluate (list x y z) solution) 'no-solution)

For CNL applications, Rosette enables:

1. Automatic planning: Describe what you want (constraints), and Rosette finds how to achieve it (a plan).

#lang rosette

(define-symbolic* action1 action2 action3 (one-of 'move 'rotate 'grab 'release));; Constraint: reach goal with exactly 3 actions(define plan-constraints (and (valid-sequence? (list action1 action2 action3)) (reaches-goal? (list action1 action2 action3))))(solve plan-constraints)

2. Verification: Prove that your agent logic satisfies safety properties.

#lang rosette

;; Verify that agent never enters forbidden zone(define-symbolic* steps integer?)(verify (assert (for/all ([step steps]) (not (in-forbidden-zone? (agent-position step))))))

3. Synthesis: Generate code that satisfies specifications.

#lang rosette

;; Synthesize a decision function(define-symbolic* threshold integer?)(define (synthesized-decide state) (if (> (state-energy state) threshold) 'active 'conserve));; Find the right threshold(synthesize #:forall (list state) #:guarantee (matches-examples? synthesized-decide))

Logic Programming and Datalog

Racket hosts several logic programming systems, with Datalog being particularly well-suited for AI agent knowledge bases and reasoning.

Datalog is a declarative query language based on logic programming. It’s perfect for expressing relational knowledge and deriving new facts through logical inference:

#lang datalog

;; Facts about agent capabilitiescapability(agent1, navigate).capability(agent1, communicate).capability(agent2, lift-heavy).capability(agent2, navigate).;; Facts about tasksrequires(task1, navigate).requires(task1, communicate).requires(task2, lift-heavy).;; Rule: an agent can perform a task if it has all required capabilitiescan-perform(Agent, Task) :- requires(Task, Cap), capability(Agent, Cap).;; Query: which agents can perform task1?can-perform(Agent, task1)?

For AI agents, Datalog enables powerful reasoning patterns:

Knowledge base queries:

#lang datalog

;; Spatial relationshipsadjacent(room1, room2).adjacent(room2, room3).adjacent(room3, room4).;; Transitive closure: reachabilityreachable(X, Y) :- adjacent(X, Y).reachable(X, Z) :- adjacent(X, Y), reachable(Y, Z).;; Query: can we reach room4 from room1?reachable(room1, room4)?

Trust and reputation:

#lang datalog

;; Trust relationshipstrusts(alice, bob).trusts(bob, charlie).;; Delegation ruledelegates(X, Z) :- trusts(X, Y), trusts(Y, Z).;; Query: transitive trustdelegates(alice, charlie)? ; Yes

Temporal reasoning:

#lang datalog

;; Events with timestampsevent(agent1, move, 100).event(agent1, communicate, 150).event(agent2, move, 120).;; Rule: events that happened before another eventbefore(E1, E2) :- event(A1, E1, T1), event(A2, E2, T2), T1 < T2.

Datalog’s bottom-up evaluation and efficient join algorithms make it ideal for real-time agent reasoning over large knowledge bases.

Natural Constraint Languages for AI Agents

Now let’s put it all together with practical examples of CNLs for AI agent systems:

1. Agent Policy DSL

#lang racket

(require rosette/safe);; Define a natural policy language(define-syntax-rule (policy name [condition action] ...) (define (name state) (cond [condition action] ... [else 'no-action])));; Usage with natural constraints(policy battery-management [(< battery-level 20) 'return-to-base] [(< battery-level 50) 'conserve-energy] [(and (> battery-level 80) (task-pending?)) 'execute-task] [else 'patrol]);; Policy composition(define (combined-policy state) (let ([battery-action (battery-management state)] [threat-action (threat-response state)]) (resolve-conflict battery-action threat-action)))

2. Temporal Constraint Language

#lang racket

(require gregor) ; DateTime library;; Temporal CNL for agent scheduling(define-syntax (schedule stx) (syntax-parse stx [(_ task:id "every" interval:number unit:id "between" start:expr "and" end:expr) #'(make-recurring-task 'task (interval interval 'unit) start end)]));; Usage(schedule patrol-route "every" 30 minutes "between" #:time "06:00" "and" #:time "22:00")(schedule status-report "every" 1 hour "between" (today) "and" (days-from-now 7));; Constraint checking with Rosette#lang rosette(define-symbolic* t1 t2 t3 integer?)(verify (assert ;; No two tasks overlap (and (< t1 t2) (< t2 t3)) ;; All within working hours (and (>= t1 360) (<= t3 1320)))) ; 6am to 10pm in minutes

3. Resource Allocation Language

#lang rosette

;; Natural language for resource constraints(define-syntax (allocate stx) (syntax-parse stx [(_ resource:id "to" agent:id "with" "priority" p:number "when" condition:expr) #'(make-allocation 'resource 'agent #:priority p #:condition condition)]));; Define symbolic resources(define-symbolic* cpu-agent1 cpu-agent2 cpu-agent3 integer?);; Constraints in natural form(allocate cpu "to" agent1 "with" "priority" 10 "when" (critical-task-active?));; Verify resource constraints(verify (assert (and ;; Total CPU <= 100% (<= (+ cpu-agent1 cpu-agent2 cpu-agent3) 100) ;; Each agent gets minimum (>= cpu-agent1 20) (>= cpu-agent2 20) (>= cpu-agent3 20) ;; High priority agent gets most (=> (critical-task-active?) (>= cpu-agent1 50)))))

4. Multi-Agent Coordination Protocol

#lang racket

(require datalog);; Protocol rules in natural constraint form(define protocol '( ;; Leadership rules (leader(Agent) :- highest-priority(Agent) available(Agent)) ;; Task assignment (assigned(Agent, Task) :- capable(Agent, Task) not(assigned(_, Task)) available(Agent)) ;; Coordination constraint (mutex(Task1, Task2) :- requires(Task1, Resource) requires(Task2, Resource) limited(Resource)) ;; Deadlock prevention (safe-state() :- not( and( waiting(Agent1, Agent2) waiting(Agent2, Agent1)))) ));; Integration with Rosette for verification#lang rosette(define-symbolic* agent1-task agent2-task (one-of 'taskA 'taskB 'taskC))(verify (assert ;; No deadlock (safe-state) ;; Progress guarantee (or (assigned? agent1-task) (assigned? agent2-task))))

5. Behavioral Constraints with Dependent Types

#lang cur

;; Define agent states with proofs(define-type AgentState (Σ ([battery : Nat] [position : (Pair Nat Nat)] [carrying : (Maybe Object)]) ;; Invariants as types (and (<= battery 100) (=> (just? carrying) (>= battery 30)))));; Actions with preconditions(define-type (MoveAction (s : AgentState)) (Π ([direction : Direction]) (-> (>= (fst s) 10) ; Battery > 10% AgentState)));; Protocol that maintains invariants(define/rec/match safe-move : (Π ([s : AgentState]) (MoveAction s)) [(s direction battery-proof) ;; Compiler verifies battery-proof is valid (update-position s direction)])

6. Complete Agent CNL Example

Here’s a comprehensive example combining multiple features:

#lang racket

(require rosette/safe datalog gregor);; Define agent behavior DSL(define-syntax (agent-program stx) (syntax-parse stx [(_ name:id #:knowledge [fact ...] #:rules [rule ...] #:policy [policy-clause ...] #:schedule [schedule-item ...]) #'(begin ;; Knowledge base (define name-kb (datalog fact ... rule ...)) ;; Policy engine (define (name-policy state) (cond policy-clause ... [else 'idle])) ;; Scheduler (define name-schedule (list schedule-item ...)) ;; Main agent loop (define (name state) (let* ([knowledge (query name-kb state)] [action (name-policy state)] [scheduled (check-schedule name-schedule)]) (combine-actions knowledge action scheduled))))]));; Usage(agent-program warehouse-robot #:knowledge [(location(self, warehouse-a).) (capacity(self, 50).) (available(self).)] #:rules [(can-carry(Item) :- capacity(self, Cap) weight(Item, W) (< W Cap).) (optimal-path(From, To, Path) :- reachable(From, To) shortest(From, To, Path).)] #:policy [[(and (low-battery? state) (< (distance-to-charger state) 100)) 'navigate-to-charger] [(task-assigned? state) 'execute-task] [(idle? state) 'patrol]] #:schedule [(every 1 hour report-status) (every 30 minutes check-inventory) (at #:time "02:00" run-diagnostics)])

Why Racket Excels at Constraint Natural Language

Racket’s unique position as a programmable programming language makes it ideal for CNL development:

Grammar flexibility: The #lang mechanism and reader macros let you design syntax that matches your domain, from S-expressions to natural language.

Composable language features: Libraries extend the language itself, allowing you to mix temporal logic, constraints, types, and logic programming seamlessly.

Gradual guarantees: Start with dynamic prototypes, add types where needed, use dependent types for critical invariants, and employ solvers for automatic verification — all in the same ecosystem.

Industrial-strength tooling: Racket comes with DrRacket IDE, package management, documentation generation, testing frameworks, and a mature ecosystem.

Research platform: Racket is actively used in PL research, meaning cutting-edge ideas (like Rosette, Cur, and advanced macro systems) appear in Racket first.

For AI agents operating in complex, constrained environments, CNLs provide the abstraction needed to reason about behavior at the right level. You’re not debugging pointer arithmetic or wrestling with type systems — you’re expressing agent policies, coordination protocols, and safety constraints in domain-appropriate terms.

Racket gives you the tools to build those languages, from simple DSLs to sophisticated constraint systems with formal guarantees. Whether you need quick prototypes or verified systems with mathematical proofs of correctness, Racket’s programmable nature adapts to your needs.

Future Directions

The intersection of CNL and AI agents is ripe for innovation:

Learning-enhanced CNLs: Combining symbolic constraint languages with neural learning for adaptive agent behaviors

Multi-paradigm integration: Blending logic programming, constraint solving, and probabilistic reasoning in unified CNLs

Verified agent coordination: Using dependent types and solver-aided verification to prove protocol properties

Natural language interfaces: Leveraging LLMs to translate human instructions into formal CNL specifications

Distributed constraint solving: Extending Rosette-style reasoning to multi-agent coordination problems

Racket’s architecture positions it uniquely to explore these directions. When your programming language is programmable, when libraries are language extensions, when types can prove theorems, and when solvers can synthesize code — the boundaries between specification and implementation blur in productive ways.

The future of AI agents isn’t just smarter algorithms; it’s better languages for expressing what we want agents to do. Racket shows us how to build those languages.